In the roiling waters of the northern sea, a mysterious oil rig has appeared whose lights can be seen for miles in the darkness. The foreman is a bit jittery, clearly worried about the platform's somewhat shoddy construction and his overbearing boss docking his pay for being behind schedule. When the drilling does begin, it's not oil they strike, but something far more sinister that splinters the sea floor with an ominous red, volcanic glow. And the resident scientist and his corporate suit boss only seem to want to feed this supernatural terror, the safety of their fellow workers be damned.

To some, I've just described the plot of The Chinese Room's recent walking horror, Still Wakes the Deep. But in a strange coincidence, Dredge is now following the same trajectory with its latest DLC, The Iron Rig - albeit with fewer left hooks throwing errant Coke cans to distract wandering nasties and more actual hooks reeling up yet more fresh horrors from the deep. This new chapter in Dredge's faintly cursed archipelago finally puts the spotlight on the mysterious Ironhaven Corporation, whose CEO is a dead ringer for Wake's weaselly boss Rennick, right down to his last-minute escape on the corporate helicopter. It's all profits before people in this unfortunate corner of the ocean, though unlike Wake's poor Caz, at least you can share in some of those benefits this time thanks to the plentiful supply of upgrade opportunities for your boat.

Over the course of three to four hours, The Iron Rig will see you revisit each of Dredge's main island clusters, hunting for fresh fish in new micro-habitats created by pools of ungodly ooze that have sprung up from the fissures created by the rig's drill. Each time one of these clearly very bad cracks opens up, the rig's scientist will task you with collecting various samples to bring back to him for further analysis, but some of these aren't easily won with your default set of equipment. Cue, then, a gentle and well-paced upgrade back and forth that will see you construct new buildings on the rig to unlock enhanced versions of your rods, nets and winches, as well as blueprints for new engine parts, bait types and other gadgets to help speed the process along.

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The story content of DREDGE: The Iron Rig revolves around finding the new strange specimens in all the regions whose waters get contaminated with oil as the story progresses. Upon bringing the strange specimens of Twisted Strand (the fourth contaminated area) to the Scientist, the contamination will spread to the final region, Devil’s Spine, requiring you to bring more strange specimens.

This guide will help you get all strange specimens of Devil’s Spine in DREDGE: The Iron Rig.

How to Catch All Strange Specimens of Devil’s Spine in DREDGE: The Iron Rig

The Strange Specimens of Devil’s Spine are of Volcanic and Hadal kinds only one of which can be caught using the Infused Hoist, but for the Volcanic kind, you have to craft a new rod called Infused Fireproof Rod. Crafting the Infused Fireproof Rod will require the following materials and a $200 cost.

• 1x Ironhaven Crate
• 1x Dark Canister
• 1x Heat-Resistant Line

NOTE: The Ironhaven Crate is found at the missing ship locations marked on the map. The Dark Canister is caught by sailing over the contaminated areas while Siphon Trawler is equipped. Lastly, the Heat-Resistant Line can be purchased from the Undermarket facility at the Iron Rig.

After crafting the Infused Fireproof Rod, equip it along with Infused Hoist and set sail to the Devil’s Spine to find and catch the strange specimens.

Where to Find the Strange Specimens of Devil’s Spine in DREDGE: The Iron Rig

All the strange specimens are found in the contaminated waters of Devil’s Spine so, look for any fish bubbles under the oil. The spawn location of every specimen is mentioned in the list below.

• Giant Dragonfish – A Hadal (type) fish found at the N-14 coordinates.
• Vetulicolia – A Volcanic (type) fish found also at the Q-12 coordinates.
• Dunkleosteus – A Volcanic (type) fish also found at the Q-12 coordinates.
• Sea Scorpion – it is an Infused Crab that can be found at the P-14 coordinates. Leave a Crab Pot at the location and pick it up after some time to get the Sea Scorpion.

Once you have obtained all the Specimens, the only thing left to do is to deliver them to the scientist and complete the fifth and final part of the Strange Specimens, the main pursuit.

Moreover, you will witness the Scientist turning into a contaminated creature, jumping out of the window, and disappearing into the Deep.   

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The Sabotage is a main pursuit in The Iron Rig expansion of DREDGE that players will initiate after delivering all strange specimens of Twisted Strand. Doing so will make the Executive force the Foreman to start the drill, disturbing the creature below. The Foreman asks for your help to reason with the Executive.

This guide will walk you through the Sabotage pursuit in DREDGE: The Iron Rig.

How to Complete the Sabotage (Main Pursuit) in DREDGE: The Iron Rig

Check the Defenses Facility

As the Sabotage pursuit begins, the first objective is to check the Defenses facility. Select the first option to open one of the electrical panels to find a black box. However, you can’t do anything with it due to a lack of knowledge and the game will tell you to visit the Researcher, the Scientist’s student.

Meet the Researcher

Leave the Iron Rig and set sail to the Old Fortress location in the Stellar Basin region. Dock at the Old Fortress to meet with the Researcher and select the “About those defenses on the rig…” > “The workers on the rig want to find a way to sabotage them,” dialogues. Doing so will give you the blueprints for sabotaging the defenses to attract the creatures to the Rig.

Install a New Module to the Defense System

The next step is to install a new module ‘Rise’ to the defense system on the Iron Rig, but it will require a Dark Canister and an Ironhaven Crate. Luckily, you can get both of these materials on your way to the Iron Rig. Make sure to grab them and then return to the Iron Rig.

Dock at the Rig and select the ‘Defenses’ facility to install the module by delivering both of the materials. It will attract back the tentacle creature damaging the base of the rig. However, another creature will appear and attack the tentacle creature before it does any further damage to the rig.

As the cutscene ends, the Executive will run away from the rig and the pursuit will be resolved.

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AAEON first showed off the Intel Core-powered UP Squared i12 Edge in October of last year. Now the company has units in stock and ready to ship. The i12 Edge is a compact PC that measures 130 x 94 x 68mm. It’s designed to be deployed in demanding environments, with operating temperatures between 32º and […]

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With Sony having surprised precisely no-one by announcing that, once again, PlayStation won't be at this year's Gamescom, Microsoft has seized the opportunity to let everyone know that, by Jove, Xbox will be at this year's show.

Microsoft shared the news over on social media, inviting attendees to visit its "biggest booth yet" when Gamescom returns to Cologne, Germany, in August this year.

Microsoft will, of course, be the only major console maker to have at presence at Gamescom 2024; Nintendo confirmed it wouldn't be coming along back in April, saying there would be opportunities for players to try Switch games at other events throughout the year, and PlayStation confirmed a no-show earlier this week. Sony's absence hardly warrants a raised eyebrow, however, given it hasn't showed up since 2019.

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Developer Black Salt Games has confirmed that Dredge's highly anticipated second DLC, The Iron Rig, will release on 15th August, 2024.

The indie fishing adventure will, ahem, "lure players back in" with new fish, new boat upgrades and gadgets, and a brand-new storyline.

Playable at any time in your Dredge adventure, The Iron Rig sees you join the Ironhaven Corporation to "collect resources to build a formidable base of operations, unlocking new tiers of equipment, abilities, gadgets, and consumables". And as part of the adventure, you'll get to "revisit every area from the base game with renewed purpose".

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Age Of Mythology: Retold, the remaster of the classic strategy beast-em-up, is due to release this year, 4th September 2024 on PC and Game Pass. This will be the 2002 game’s second remaster following its Extended Addition re-release in 2014, and aims to thoroughly spiff up the original with a 3D visual overhaul, new animations and texture, plus UI and QOL improvements. Prevent yourself being seduced by the suspiciously thunder-imbued swan that’s currently chatting you up, and have a gander at the trailer below:

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Large Language Models (LLMs) have taken the world by storm since the 2017 Transformers paper, but pushing them to the edge has proved problematic. Just this year, Google had to revise its plans to roll out Gemini Nano on all new Pixel models — the down-spec’d hardware options proved unable to host the model as part of a positive user experience. But the implementation of language-focused models at the edge is perhaps the wrong metric to look at. If you are forced to host a language-focused model for your phone or car in the cloud, that may be acceptable as an intermediate step in development. Vision applications of AI, on the other hand, are not so flexible: many of them rely on low latency and high dependability. If a vehicle relies on AI to identify that it should not hit the obstacle in front of it, a blip in contacting the server can be fatal. Accordingly, the most important LLMs to fit on the edge are vision models — the models whose purpose is most undermined by the reliance on remote resources.

“Large Language Models” can be an imprecise term, so it is worth defining. The original 2017 Transformer LLM that many see as kickstarting the AI rush was 215 million parameters. BERT was giant for its time (2018) at 335 million parameters. Both of these models might be relabeled as “Small Language Models” by some today to distinguish from models like GPT4 and Gemini Ultra with as much as 1.7 trillion parameters, but for the purposes here, all fall under the LLM category. All of these are language models though, so why does it matter for vision? The trick here is that language is an abstract system of deriving meaning from a structured ordering of arbitrary objects. There is no “correct” association of meaning and form in language which we could base these models on. Accordingly, these arbitrary units are substitutable — nothing forces architecture developed for language to only be applied to language, and all the language objects are converted to multidimensional vectors anyway. LLM architecture is thus highly generalizable, and typically retains the core strength from having been developed for language: a strong ability to carry through semantic information. Thus, when we talk about LLMs at the edge, it can be a language model cross-trained on image data, or it might be a vision-only model which is built on the foundation of technology designed for language. At the software and hardware levels, for bringing models to the edge, this distinction makes little difference.

Vision LLMs on the edge flexibly apply across many different use cases, but key applications where they show the greatest advantages are: embodied agents (an especially striking example of the benefits of cross-training embodied agents on language data can be seen with Dynalang’s advantages over DreamerV3 in interpreting the world due to superior semantic parsing), inpainting (as seen with the latent diffusion models), LINGO-2’s decision-making abilities in self-driving vehicles, context-aware security (such as ViViT), information extraction (Gemini’s ability to find and report data from video), and user assistance (physician aids, driver assist, etc). Specifically notable and exciting here is the ability for Vision LLMs to leverage language as a lossy storage and abstraction of visual data for decision-making algorithms to then interact with — especially as seen in LINGO-2 and Dynalang. Many of these vision-oriented LLMs depend on edge deployment to realize their value, and they benefit from the work that has already been done for optimizing language-oriented LLMs. Despite this, vision LLMs are still struggling for edge deployment just as the language-oriented models are. The improvements for edge deployments come in three classes: model architecture, system resource utilization, and hardware optimization. We will briefly review the first two and look more closely at the third since it often gets the least attention.

Model architecture optimizations include the optimizations that must be made at the model level: “distilling” models to create leaner imitators, restructuring where models spend their resource budget (such as the redistribution of transformer modules in Stable Diffusion XL) and pursuing alternate architectures (state-space models, H3 modules, etc.) to escape the quadratically scaling costs of transformers.

System resource optimizations are all the things that can be done in software to an already complete model. Quantization (to INT8, INT4, or even INT2) is a common focus here for both latency and memory burden, but of course compromises accuracy. Speculative decoding can improve utilization and latency. And of course, tiling, such as seen with FlashAttention, has become near-ubiquitous for improving utilization and latency.

Finally, there are hardware optimizations. The first option here is a general-purpose GPU, TPU, NPU or similar, but those tend to be best suited for settings where capability is needed without demanding streamlined optimization such as might be the case on a home computer. Custom hardware, such as purpose-built NPUs, generally has the advantage when the application is especially sensitive to latency or resource consumption, and this covers much of the applications for vision LLMs.

Exploring this trade-off further: Stable Diffusion’s architecture and resource demands have been discussed here before, but it is worth circling back to it as an example of why hardware solutions are so important in this space. Using Stable Diffusion 1.5 for simplicity, let us focus specifically on the U-Net component of the model. In this diagram, you can see the rough construction of the model: it downsamples repeatedly on the left until it hits the bottom of the U, and then upsamples up the right side, bringing back in residual connections from the left at each stage.

This U-Net implementation has 865 million parameters and entails 750 billion operations. The parameters are a fair proxy for the memory burden, and the operations are a direct representation of the compute demands. The distribution of these burdens on resources is not even however. If we plot the parameters and operations for each layer, a clear picture emerges:

These graphs show a model that is destined for gross inefficiencies at every step. Most of the memory burden peaks in the center, whereas the compute is heavily taxed at the two tails but underutilized in the center. These inefficiencies come with costs. The memory peak can overwhelm on-chip storage, thus incurring I/O operations, or else requiring a large excess of unused memory for most of the graph. Similarly, storing residuals for later incurs I/O latency and higher power draws. The underutilization of the compute power at the center of the graph means that the processor will have wasteful power draw as it cannot use the tail of the power curve as it does sparser operations. While software interventions can also help here, this is exactly the kind of problem that custom hardware solutions are meant to address. Custom silicon tailored to the model can let you offload some of that memory burden into additional compute cycles at the center of the graph without incurring extra I/O operations by recomputing the residual connections instead of kicking them out to memory. In doing so, the total required memory drops, and the processor can remain at full utilization. Rightsizing the resource allotment and finding ways to redistribute the burdens are key components to how these models can be best deployed at the edge.

Despite their name, LLMs are important to the vision domain for their flexibility in handling different inputs and their strength at interpreting meaning in images. Whether used for embodied agents, context-aware security, or user assistance, their use at the edge requires a dependable low latency which precludes cloud-based solutions, in contrast to other AI applications on edge devices. Bringing them successfully to the edge asks for optimizations at every level, and we have seen already some of the possibilities at the hardware level. Conveniently, the common architecture with language-oriented LLMs means that many of the solutions needed to bring these most essential models to the edge in turn may also generalize back to the language-oriented models which donated the architecture in the first place.

The post Vision Is Why LLMs Matter On The Edge appeared first on Semiconductor Engineering.

Samsung unveiled the Galaxy Book 4 Edge earlier this month. It's the company's first laptop that's powered by the Qualcomm Snapdragon X Elite. As you'd expect, AI is front and center on this product, with the Galaxy Book 4 Edge boasting some Galaxy AI features.

The company has started taking pre-orders for the device in some markets already. The launch information was limited for its home market of South Korea, but Samsung has now provided clarity on the matter.

Samsung's first Snapdragon X Elite laptop coming next month

Samsung confirmed during a media day event for the product yesterday that it's going to launch the Galaxy Book 4 Edge in South Korea next month, June 18th, to be precise. Pricing starts at 2.15 million won or $1,563 for the 14-inch variant, with other models in larger sizes costing a bit more.

It's a pretty capable notebook, powered by the Snapdragon X Elite and a dedicated NPU for 45 TOPS of AI performance. An ARM64-optimized version of Windows 11 is onboard with the promise of exceptional battery life. The chipset is paired with 16GB of RAM and 512GB of storage. Other specs include a Dynamic AMOLED 2X touchscreen display with 3K resolution, a 120Hz refresh rate, and a 120% color gamut average.

A handful of AI features are also present on the device. This includes Circle to Search via Link to Windows, access to Windows Copilot with a dedicated key, a new Recall feature that lets users search for recently created emails, videos, or documents, Live Captions with translation, Windows Studio Effects during video calls, and more.

The post Galaxy Book 4 Edge release in South Korea set for June appeared first on SamMobile.

Microsoft has been busy adding a ton of AI-powered features to its Edge browser lately. We recently saw Microsoft adding AI-generated themes to Edge to give users a truly unique background for their browsing, and now, another AI-powered feature has appeared within Edge's code. While nothing has been outright confirmed yet, there is evidence of work being done on a website suggestion feature that'd use AI to guide your browsing.

Generative AI (GenAI) burst onto the scene and into the public’s imagination with the launch of ChatGPT in late 2022. Users were amazed at the natural language processing chatbot’s ability to turn a short text prompt into coherent humanlike text including essays, language translations, and code examples. Technology companies – impressed with ChatGPT’s abilities – have started looking for ways to improve their own products or customer experiences with this innovative technology. Since the ‘cost’ of adding GenAI includes a significant jump in computational complexity and power requirements versus previous AI models, can this class of AI algorithms be applied to practical edge device applications where power, performance and cost are critical? It depends.

What is GenAI?

A simple definition of GenAI is ‘a class of machine learning algorithms that can produce various types of content including human like text and images.’ Early machine learning algorithms focused on detecting patterns in images, speech or text and then making predictions based on the data. For example, predicting the percentage likelihood that a certain image included a cat. GenAI algorithms take the next step – they perceive and learn patterns and then generate new patterns on demand by mimicking the original dataset. They generate a new image of a cat or describe a cat in detail.

While ChatGPT might be the most well-known GenAI algorithm, there are many available, with more being released on a regular basis. Two major types of GenAI algorithms are text-to-text generators – aka chatbots – like ChatGPT, GPT-4, and Llama2, and text-to-image generative model like DALLE-2, Stable Diffusion, and Midjourney. You can see example prompts and their returned outputs of these two types of GenAI models in figure 1. Because one is text based and one is image based, these two types of outputs will demand different resources from edge devices attempting to implement these algorithms.

Fig. 1: Example GenAI outputs from a text-to-image generator (DALLE-2) and a text-to-text generator (ChatGPT).

Edge device applications for Gen AI

Common GenAI use cases require connection to the internet and from there access to large server farms to compute the complex generative AI algorithms. However, for edge device applications, the entire dataset and neural processing engine must reside on the individual edge device. If the generative AI models can be run at the edge, there are potential use cases and benefits for applications in automobiles, cameras, smartphones, smart watches, virtual and augmented reality, IoT, and more.

Deploying GenAI on edge devices has significant advantages in scenarios where low latency, privacy or security concerns, or limited network connectivity are critical considerations.

Consider the possible application of GenAI in automotive applications. A vehicle is not always in range of a wireless signal, so GenAI needs to run with resources available on the edge. GenAI could be used for improving roadside assistance and converting a manual into an AI-enhanced interactive guide. In-car uses could include a GenAI-powered virtual voice assistant, improving the ability to set navigation, play music or send messages with your voice while driving. GenAI could also be used to personalize your in-cabin experience.

Other edge applications could benefit from generative AI. Augmented Reality (AR) edge devices could be enhanced by locally generating overlay computer-generated imagery and relying less heavily on cloud processing. While connected mobile devices can use generative AI for translation services, disconnected devices should be able to offer at least a portion of the same capabilities. Like our automotive example, voice assistant and interactive question-and-answer systems could benefit a range of edge devices.

While uses cases for GenAI at the edge exist now, implementations must overcome the challenges related to computational complexity and model size and limitations of power, area, and performance inherent in edge devices.

What technology is required to enable GenAI?

To understand GenAI’s architectural requirements, it is helpful to understand its building blocks. At the heart of GenAI’s rapid development are transformers, a relatively new type of neural network introduced in a Google Brain paper in 2017. Transformers have outperformed established AI models like Recurrent Neural Networks (RNNs) for natural language processing and Convolutional Neural Networks (CNNs) for images, video or other two- or three-dimensional data. A significant architectural improvement of a transformer model is its attention mechanism. Transformers can pay more attention to specific words or pixels than legacy AI models, drawing better inferences from the data. This allows transformers to better learn contextual relationships between words in a text string compared to RNNs and to better learn and express complex relationships in images compared to CNNs.

Fig. 2: Parameter sizes for various machine learning algorithms.

GenAI models are pre-trained on vast amounts of data which allows them to better recognize and interpret human language or other types of complex data. The larger the datasets, the better the model can process human language, for instance. Compared to CNN or vision transformer machine learning models, GenAI algorithms have parameters – the pretrained weights or coefficients used in the neural network to identify patterns and create new ones – that are orders of magnitude larger. We can see in figure 2 that ResNet50 – a common CNN algorithm used for benchmarking – has 25 million parameters (or coefficients). Some transformers like BERT and Vision Transformer (ViT) have parameters in the hundreds of millions. While other transformers, like Mobile ViT, have been optimized to better fit in embedded and mobile applications. MobileViT is comparable to the CNN model MobileNet in parameters.

Compared to CNN and vision transformers, ChatGPT requires 175 billion parameters and GPT-4 requires 1.75 trillion parameters. Even GPUs implemented in server farms struggle to execute these high-end large language models. How could an embedded neural processing unit (NPU) hope to complete so many parameters given the limited memory resources of edge devices? The answer is they cannot. However, there is a trend toward making GenAI more accessible in edge device applications, which have more limited computation resources. Some LLM models are tuned to reduce the resource requirements for a reduced parameter set. For example, Llama-2 offers a 70 billion parameter version of their model, but they also have created smaller models with fewer parameters. Llama-2 with seven billion parameters is still large, but it is within reach of a practical embedded NPU implementation.

There is no hard threshold for generative AI running on the edge, however, text-to-image generators like Stable Diffusion with one billion parameters can run comfortably on an NPU. And the expectation is for edge devices to run LLMs up to six to seven billion parameters. MLCommons have added GPT-J, a six billion parameter GenAI model, to their MLPerf edge AI benchmark list.

Running GenAI on the edge

GenAI algorithms require a significant amount of data movement and computation complexity (with transformer support). The balance of those two requirements can determine whether a given architecture is compute-bound – not enough multiplications for the data available – or memory bound – not enough memory and/or bandwidth for all the multiplications required for processing. Text-to-image has a better mix of compute and bandwidth requirements – more computations needed for processing two dimensional images and fewer parameters (in the one billion range). Large language models are more lopsided. There is less compute required, but a significantly large amount of data movement. Even the smaller (6-7B parameter) LLMs are memory bound.

The obvious solution is to choose the fastest memory interface available. From figure 3, you can see that a typically memory used in edge devices, LPDDR5, has a bandwidth of 51 Gbps, while HBM2E can support up to 461 Gbps. This does not, however, take into consideration the power-down benefits of LPDDR memory over HBM. While HBM interfaces are often used in high-end server-type AI implementations, LPDDR is almost exclusively used in power sensitive applications because of its power down abilities.

Fig. 3: The bandwidth and power difference between LPDDR and HBM.

Using LPDDR memory interfaces will automatically limit the maximum data bandwidth achievable with an HBM memory interface. That means edge applications will automatically have less bandwidth for GenAI algorithms than an NPU or GPU used in a server application. One way to address bandwidth limitations is to increase the amount of on-chip L2 memory. However, this impacts area and, therefore, silicon cost. While embedded NPUs often implement hardware and software to reduce bandwidth, it will not allow an LPDDR to approach HBM bandwidths. The embedded AI engine will be limited to the amount of LPDDR bandwidth available.

Implementation of GenAI on an NPX6 NPU IP

The Synopsys ARC NPX6 NPU IP family is based on a sixth-generation neural network architecture designed to support a range of machine learning models including CNNs and transformers. The NPX6 family is scalable with a configurable number of cores, each with its own independent matrix multiplication engine, generic tensor accelerator (GTA), and dedicated direct memory access (DMA) units for streamlined data processing. The NPX6 can scale for applications requiring less than one TOPS of performance to those requiring thousands of TOPS using the same development tools to maximize software reuse.

The matrix multiplication engine, GTA and DMA have all been optimized for supporting transformers, which allow the ARC NPX6 to support GenAI algorithms. Each core’s GTA is expressly designed and optimized to efficiently perform nonlinear functions, such as ReLU, GELU, sigmoid. These are implemented using a flexible lookup table approach to anticipate future nonlinear functions. The GTA also supports other critical operations, including SoftMax and L2 normalization needed in transformers. Complementing this, the matrix multiplication engine within each core can perform 4,096 multiplications per cycle. Because GenAI is based on transformers, there are no computation limitations for running GenAI on the NPX6 processor.

Efficient NPU design for transformer-based models like GenAI requires complex multi-level memory management. The ARC NPX6 processor has a flexible memory hierarchy and can support a scalable L2 memory up to 64MB of on chip SRAM. Furthermore, each NPX6 core is equipped with independent DMAs dedicated to the tasks of fetching feature maps and coefficients and writing new feature maps. This segregation of tasks allows for an efficient, pipelined data flow that minimizes bottlenecks and maximizes the processing throughput. The family also has a range of bandwidth reduction techniques in hardware and software to maximize bandwidth.

In an embedded GenAI application, the ARC NPX6 family will only be limited by the LPDDR available in the system. The NPX6 successfully runs Stable Diffusion (text-to-image) and Llama-2 7B (text-to-text) GenAI algorithms with efficiency dependent on system bandwidth and the use of on-chip SRAM. While larger GenAI models could run on the NPX6, they will be slower – measured in tokens per second – than server implementations. Learn more at www.synopsys.com/npx

The post How To Successfully Deploy GenAI On Edge Devices appeared first on Semiconductor Engineering.

Samsung is expected to unveil its first laptop featuring Qualcomm's Snapdragon X Elite processor later today. This upcoming laptop is called the Galaxy Book 4 Edge, and its pricing and specifications were leaked over the past week. Now, details regarding the laptop's Bluetooth connectivity have been revealed.

Galaxy Book 4 Edge features Bluetooth 5.4

Ahead of the launch, the Galaxy Book 4 Edge appears to have passed through the Bluetooth SIG certification. According to the Bluetooth certification authority's database, the Galaxy Book 4 Edge has model number NP940XMA and features Bluetooth 5.4 connectivity. This upcoming laptop likely uses the Snapdragon X Elite chipset's FastConnect 7800 subsystem for wireless connectivity. It features a built-in 5G modem, GPS, Wi-Fi 7, Bluetooth 5.4, and USB 4.0 (Thunderbolt 3).

This listing also reveals that the Galaxy Book 4 Edge runs Windows 11. Previously leaked images of the laptop have revealed that its 14-inch version has a tenkeyless keyboard layout, a large trackpad, two USB 4.0 ports, an HDMI port, and a 3.5mm headphone jack. The 16-inch version has a full-sized keyboard with a numpad, a USB Type-A port, and a microSD card slot in addition to all the ports on the 14-inch version.

Galaxy Book 4 Edge pricing and specifications

Both laptops feature the Snapdragon X Elite processor with 12 CPU cores and the Adreno GPU. However, the 14-inch version might use a variant of the Snapdragon X Elite with a lower clock speed for the CPU and the GPU. Both laptops reportedly feature 16GB RAM and 512GB storage. It is unclear if these laptops will be available with higher RAM and storage capacities.

You can watch our Galaxy Book 4 Ultra hands-on video below. The story continues after this video.

According to some reports, the Galaxy Book 4 Edge Pro (16-inch) can last up to 22 hours on a single charge for video playback. This laptop version uses a 61.8Wh battery and features 65W fast charging. However, these figures are for offline 1080p video playback (150 nits screen brightness and Airplane Mode turned on). Real-life battery life will likely be half of the quoted figures, but even that is impressive.

The 14-inch version of the Galaxy Book 4 Edge will likely have a smaller battery. Both variants feature Super AMOLED screens with 3K resolution and 400 nits peak brightness. They will likely come with a 120Hz refresh rate and DisplayHDR 400 certification with HDR10+ video playback capabilities.

The Galaxy Book 4 Edge might start at around $1,800, while the Galaxy Book 4 Edge Pro might cost around $2,000. These laptops are said to go on pre-order today in some countries and become available for purchase on June 17, 2024.

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If you want to maximize Microsoft Copilot’s capabilities, you can enhance query responses with plugins like Spotify and Adobe. Recently, a new plugin called “Phone” ...

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Depending on why you ask, there are numerous reasons why some people believe that Microsoft Edge is the best web browser. On the contrary, though, there is another set of people that outright reject it for issues like not being able to easily change the default browser as well as the search engine. While time will tell whether Microsoft manages to address major issues in the Edge browser, the software giant is actively working on removing the creator-tracking feature for allegedly putting users' privacy at risk.

Microsoft is poised to revolutionize the online form-filling experience with a groundbreaking update to its Edge browser. This update seamlessly integrates artificial intelligence (AI) into ...

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The Razer Edge is pretty unique in the handheld market, more akin to an Android tablet with a Kishi controller than a console. If you’ve got a commute to fill or a gamer gift to get, you won’t want to miss this Amazon deal on the device, which drops the price to just $249.99 for the first time ever.

Razer Edge for $249.99 ($150 off)

The Playdate is an unusual and ambitious handheld game console. The $199 console has a 1-bit display and a set of simple controls that includes a hand crank. Instead of enabling support for existing games, the folks behind the Playdate partnered with indie game developers to create a bunch of new games for the console, […]

The post Lilbits: Playdate’s growing game catalog, Zotac’s ZBOX PRO eGPU, and Framework’s firmware woes appeared first on Liliputing.

Yesterday, we brought to light a trademark application pertaining to a new Samsung device called the Book 4 Edge. We thought about it for a bit and guessed that the Book 4 Edge might be Samsung's first Windows 11 on ARM laptop. Now, an online benchmark is lending weight to our prediction.

The Galaxy Book 4 Edge by Samsung popped up in the online benchmark Geekbench earlier today (via Windows Latest). The laptop runs Windows 11 Home (64-bit) and is powered by the Snapdragon X Elite CPU and 16GB of RAM.

Qualcomm previously said it is looking for partners for its new Snapdragon X Elite chip, and this benchmark, if legitimate, confirms that Samsung is on the list.

Conversely, this benchmark also seems to indicate that Samsung is not yet ready to create its own Exynos ARM-based chip for laptops. And that's probably a good thing at the moment, seeing how the company is busy proving that it sorted out Exynos for mobile phones.

Plenty of power for Windows on ARM devices

Synthetic benchmark scores for the Galaxy Book 4 Edge laptop powered by the Snapdragon X Elite chip look promising. The laptop scored 2,706 points in single-core and 12,646 points in multi-core tests in Geekbench 6.2.2.

Comparing these scores to Apple's M1, M2, and M3 chips, it looks like the Snapdragon X Elite beats the first two but is outperformed by the M3. Apple M Pro chips excluded.

Of course, in practice, the M and Snapdragon X Elite chips run in very different environments, i.e., MacOS and Windows 11 on ARM, which means these benchmark figures might not necessarily say which chip performs better in the real world. Apple has optimized its M chips for MacOS very well, and it remains to be seen if we can say the same about the Snapdragon X Elite and Windows 11 on ARM.

Interestingly, recent rumors say Qualcomm's new chip will also power the upcoming Microsoft Surface Laptop 6 and Surface Pro 10 tablet PC. Samsung's Galaxy Book 4 Edge will inevitably compete with Microsoft's Surface line, but whether this Book 4 Edge is a regular notebook, a 2-in-1 with a 360-degree hinge, or a tablet PC with a detachable keyboard is to be determined.

The post Galaxy Book 4 Edge ARM chip confirmed by online benchmark appeared first on SamMobile.

The Motorola Edge Plus (2023) was possibly the best flagship phone Motorola has released in years. We won’t know if its successor will surpass it until we have it in our hands. But until then, a new leak has given us a good look at the upcoming phone’s design while also providing a few new details.

The folks over at Android Headlines have obtained renders of what’s believed to be the Motorola Edge 50 Pro. In the US, this phone will likely be called the Motorola Edge Plus (2024). While in China, it will go by the name of the Motorola X50 Ultra.

We're witnessing the dawn of AI technology right now, and it's being integrated into various parts of our lives in ways we couldn't have imagined. It's true that AI was being used in some capacity in things like self-driving cars, voice assistants, and chatbots, but it wasn't as prevalent as today. It's one thing to have dedicated services like AI image generators. Still, it's completely different to have AI integrated directly into the products and services we use every day, like web browsers, many of which you may never have expected to become a reality.

Microsoft continuously seeks to enhance its Edge browser and gain a stronger foothold in the competitive web browser landscape. In this pursuit, they introduce features ...

The post Microsoft warned! Do not install the latest update for Edge appeared first on Gizchina.com.

When I did my undergrad degree in the nowadays-blissful-seeming early noughties, I swore off videogames entirely. I sternly and sorrowfully turned my back on such hit releases as Shadow of the Colossus, Far Cry and yes, even, that PC gaming essential Half-Life 2, so as to spend 11-hour days boning up on Aeschylus and Samuel Johnson. Then, two weeks before my final exams, I somehow went out and bought Ensemble's Age Of Mythology.

I'm not sure why - blame the devil on my shoulder, I guess. It wasn't even a new release at that point. I managed to get good marks in the exams despite several nights of binge-playing, but what direction, in general, would my life have taken if I hadn't bought Age Of Mythology at such a fateful hour? Better or worse? Could I have been some kind of billionaire don with a Pulitzer by now, if it weren't for Age Of Mythology? These things keep me awake at night. Anyway, here's a little more info about the forthcoming reboot Age of Mythology: Retold, which broadly aims to turn this wrinkled titan of the strategy genre into a proper modern esport.

Read more

Age of Mythology Retold, the latest remaster of the god-battling Age of Empires spin-off, will finally see a release in 2024, having been revealed back in October 2022.

Read more

The post Gimme Helter MONDO Vanilli 1994 – Video 2023 appeared first on Mondo 2000.

Experts at the Table: Semiconductor Engineering sat down to discuss the impact of leading edge technologies such as generative AI in data centers, AR/VR, and security architectures for connected devices, with Michael Kurniawan, business strategy manager at Accenture; Kaushal Vora, senior director and head of business acceleration and ecosystem at Renesas Electronics; Paul Karazuba, vice president of marketing at Expedera; and Chowdary Yanamadala, technology strategist at Arm. What follows are excerpts of that conversation. Panelists were chosen by GSA’s EMTECH Interest Group. To view part one of this discussion, click here.

L-R: Accenture’s Kurniawan; Renesas’ Vora; Expedera’s Karazuba; Arm’s Yanamadala.

SE: In the past, a lot of data center applications were for things like enterprise resource planning (ERP), and those were 10- or 15-year cycles. Cycles now are 1 or 2 years at most. With ChatGPT, that’s about six months. How do companies plan for this today?

Kurniawan: In the past, businesses were very focused on just the technology. But technology is everywhere today. ERP is there to support the business initiatives, and there is a very intimate relationship between technology and business at this point. So virtually all businesses are technology businesses. We advise clients before implementing their technologies to think first about, ‘What are your business initiatives? What’s the business strategy? What’s the business imperative for where you want to go? What’s your vision?’ And then, once you understand that and get alignment from the leaders, you can think about the technology. You kind of jump back and forth, because those are really two sides of the same coin. You cannot separate them anymore. And your vision encompasses everything you want to achieve in the future while providing room for flexibility and testing out the technology plan you want to put in place to see how that supports your business vision. With every challenge comes opportunity. Our job as a consultant is really to be able to see what’s happening out there, continuously scanning the market, and trying to get ahead of the curve to advise clients.

Yanamadala: The rapid evolution of advanced technologies like generative AI can present challenges to data centers due to the short technology cycles and demanding workloads. Some of the key challenges with advanced workloads include fluctuating resource needs, because they can demand bursts of high compute. That means static resource allocation will be inefficient in handling these demands. Additionally, the growing demand for heterogenous computing can also present additional challenges in deploying a flexible compute infrastructure. Data centers are adding flexibility through adoption of containerization and virtualization. Adopting hardware-agnostic software frameworks like TensorFlow and PyTorch also can help to facilitate switching between different computing architectures. So can the development of efficient hardware and specialized AI accelerators.

SE: A lot of technology advancements are incremental, but if you get enough of these incremental improvements they can be combined in ways most people never imagined. We’ve seen systems shrink from mainframes to PCs to smart phones, and now computing is happening just about everywhere. Are we at the on the cusp of moving beyond a box, which we’ve been tethered to since the start of computing, and particularly with AR/VR.

Vora: I find it fascinating that somebody could wear a pair of glasses, get immersed in that world, and get used to it. From a user experience perspective, it seems like an extreme shift. Although I do see some play in certain verticals, it’s not clear there will be mass consumerization or adoption of this technology.

Kurniawan: Right now, generative AI is getting a lot of attention. ChatGPT captured the attention of hundreds of millions of people in 60 days. That says something. You input a prompt and you get a response back. ChatGPT is super-intuitive. It’s a technology with potential for many killer use cases. AR/VR is promising technology with upside potential, but there’s still work that needs to be done to tie that technology to the use case. Virtual reality gaming is number one, for sure. But the path to leveraging that technology to enhance how we operate other stuff still needs more clarity. That said, we recently published a white paper talking about the build-outs around the globe, driven by the combination of public incentivies and private investments. Everywhere around the world, everybody wants to build up their manufacturing facilities. We conducted interviews with semiconductor experts, and touched on AR/VR when we asked what they did during COVID when the whole world shut down. Is AR/VR like a hammer looking for nails? The overall response we got was pretty positive. They said that AR/VR probably will be tremendously useful at some future date. But they like where the technologies are going. For example, there are constraints like heat dissipation and the size of the headset, but the belief is the technology will evolve. As it matures to become more user-centric, you might think about using an AR/VR device to control the operations of the equipment in a fab. But there is work needed from a value perspective — connectivity and processing, for example.

Karazuba: AR/VR in the past has largely been a victim of its own hype cycle. There’s a lot of promises people have made. We’ve spent a little bit of time with AR/VR folks. There’s certainly an acknowledgement that whatever success the Apple AR/VR headset has will largely set the tone for the next half decade for what the AR/VR market is. These folks are not undeterred by that. Are we at a point today where you can walk around all day with mixed reality? No. With a home gaming system, being tied to the wall is probably a small price to pay for the constant AC power and the performance advantages that will provide. This is going to take some time. The value proposition is there, but the timing may not be right today. We saw this with the watch and wearables. Now, everybody has one of these. But it took five to seven years before it really took off.

Vora: We’ve worn watches for decades, so it’s not something new. It’s just that what we wear now is different. But with AR/VR, we’ve never done that before. How do you suddenly expect massive change like that?

Karazuba: But most of us are wearing eyeglasses. If you have a form factor that is a version of what we have now, where information is just simply overlaid on what we’re seeing, it’s not that far of a jump for mixed reality or augmented reality. However, with virtual reality, I find it hard to believe that people are going to walk into a conference room with a bunch of other people and put a headset on.

Yanamadala: We’ve seen devices and sensors deployed practically everywhere. Platforms that offer high-performance computing, along with secure, power-efficient hardware and connectivity are available today, and they will make this trend possible. But untethered or ambient consumer experiences in the mass market will have their challenges. We will need to invest in substantial infrastructure to enable technology to operate invisibly in the background. So while consumer-facing technology deployments increasingly become untethered, the compute and connectivity infrastructure will still require connections for power and bandwidth.

SE: People have been sounding the alarm for hardware security for years, but with limited success. What’s changed today is that we have many more connected devices and more valuable data. Is the chip industry starting to take this seriously? Or is the problem now so immense and pervasive that anything we do is just going to be a drop in the bucket?

Yanamada: Security is fundamental from the chip level, and five years ago we saw an opportunity to proactively improve the quality of chip security. IoT was in its early stages, and each chip vendor had varied and fragmented approaches to security. They also rarely approached an independent evaluation lab to check the robustness of their security implementation. But with increasing connectivity and data becoming more valuable, hackers were paying close attention, and governments were considering what action to take to protect consumers. That’s why in 2019, we launched PSA Certified – to rally the ecosystem to be proactive with security best practices. It’s critically important that chip vendors, software platforms, OEMs, and CSPs can deploy and access standardized Root of Trust services. Security is complicated. You need the whole value chain to work together.

Vora: Security architectures, at least on the hardware side, have come a long way. We pretty much now have a semiconductor TPM-like [Trusted Platform Module] capability, with security capabilities built into even small microcontrollers. They have cryptographic engines, randomizers, and all sorts of security elements built in. The fundamental challenge with security is that just putting some security features on a chip and providing all the technology pieces won’t solve the security challenge. Security is more of a system challenge and a policy challenge. In many cases, people have to think about it within the context of the entire network. And then, it’s only as strong as the weakest link in the network. That piece of security is going to grow in complexity as we start seeing more complex use cases with AI coming into play with IoT. On the other side, though, as data handling of AI moves closer to the edge, we will start seeing more local inferencing and local data being worked on without the need to mindlessly transport data across layers of networks and across the cloud. We’re going to see some lower risk and improvements from a data-in-flight perspective, because of a lot of more localization of intelligence and compute happening at different layers of the edge. As we start moving more to the edge, AI starts getting more of a hold there. But as a whole, security will remain a challenge. The fundamental challenges with security have not changed. It’s just the context and the systems in which we will have to apply them are different.

Karazuba: The semiconductor industry is finally starting to understand the true nature of what security breaches could mean with the type of data we’re handling. Security is a day zero responsibility of anyone building a product, whether that product is a chip or a device, and security responsibilities proliferate across the entire lifecycle of the of any device, from the person who is architecting the chip, to the person designing the smartphone, to the carrier. I would argue that carrier responsibilities for security go as far as the stopping those robo calls that we all get, and the spam calls and phishing calls. The internet service providers have a responsibility to stop the phishing e-mails. That’s all part of security. Obviously, with banks and financial institutions, their security is generally pretty good. But it stretches the entire way, and in the security world, the weakest link is always the security profile of your device. We’re getting better. We always could be better. But I am more encouraged now than I’ve been at any point since I really started looking at security of devices. I’m more encouraged by the way chips are being designed, deployed, manufactured, and delivered to customers.

Kurniawan: There’s some certification for IoT devices before those are sent into the market to make sure there is some security standard they adhere to. But two key words I mentioned before, collaboration and flexibility, are applicable to security, as well. Collaboration involves where you see the rest of the system, including other components in the technology set, going to evolve in the future. And flexibility is required, because security is a moving target. It needs to evolve because as you upgrade your system, your software, a vulnerability will move, as well. You need flexibility and security-minded thinking infused into your chip design.

Related Reading
Preparing For An AI-Driven Future In Chips (part 1 of above roundtable)
Designs need to be flexible enough to handle an onslaught of continuous and rapid changes, but secure enough to protect data.

The post Broad Impact From Accelerating Tech Cycles appeared first on Semiconductor Engineering.

The post Gimme Helter MONDO Vanilli 1994 – Video 2023 appeared first on Mondo 2000.

Experts at the Table: Semiconductor Engineering sat down to discuss the impact of leading edge technologies such as generative AI in data centers, AR/VR, and security architectures for connected devices, with Michael Kurniawan, business strategy manager at Accenture; Kaushal Vora, senior director and head of business acceleration and ecosystem at Renesas Electronics; Paul Karazuba, vice president of marketing at Expedera; and Chowdary Yanamadala, technology strategist at Arm. What follows are excerpts of that conversation. Panelists were chosen by GSA’s EMTECH Interest Group. To view part one of this discussion, click here.

L-R: Accenture’s Kurniawan; Renesas’ Vora; Expedera’s Karazuba; Arm’s Yanamadala.

SE: In the past, a lot of data center applications were for things like enterprise resource planning (ERP), and those were 10- or 15-year cycles. Cycles now are 1 or 2 years at most. With ChatGPT, that’s about six months. How do companies plan for this today?

Kurniawan: In the past, businesses were very focused on just the technology. But technology is everywhere today. ERP is there to support the business initiatives, and there is a very intimate relationship between technology and business at this point. So virtually all businesses are technology businesses. We advise clients before implementing their technologies to think first about, ‘What are your business initiatives? What’s the business strategy? What’s the business imperative for where you want to go? What’s your vision?’ And then, once you understand that and get alignment from the leaders, you can think about the technology. You kind of jump back and forth, because those are really two sides of the same coin. You cannot separate them anymore. And your vision encompasses everything you want to achieve in the future while providing room for flexibility and testing out the technology plan you want to put in place to see how that supports your business vision. With every challenge comes opportunity. Our job as a consultant is really to be able to see what’s happening out there, continuously scanning the market, and trying to get ahead of the curve to advise clients.

Yanamadala: The rapid evolution of advanced technologies like generative AI can present challenges to data centers due to the short technology cycles and demanding workloads. Some of the key challenges with advanced workloads include fluctuating resource needs, because they can demand bursts of high compute. That means static resource allocation will be inefficient in handling these demands. Additionally, the growing demand for heterogenous computing can also present additional challenges in deploying a flexible compute infrastructure. Data centers are adding flexibility through adoption of containerization and virtualization. Adopting hardware-agnostic software frameworks like TensorFlow and PyTorch also can help to facilitate switching between different computing architectures. So can the development of efficient hardware and specialized AI accelerators.

SE: A lot of technology advancements are incremental, but if you get enough of these incremental improvements they can be combined in ways most people never imagined. We’ve seen systems shrink from mainframes to PCs to smart phones, and now computing is happening just about everywhere. Are we at the on the cusp of moving beyond a box, which we’ve been tethered to since the start of computing, and particularly with AR/VR.

Vora: I find it fascinating that somebody could wear a pair of glasses, get immersed in that world, and get used to it. From a user experience perspective, it seems like an extreme shift. Although I do see some play in certain verticals, it’s not clear there will be mass consumerization or adoption of this technology.

Kurniawan: Right now, generative AI is getting a lot of attention. ChatGPT captured the attention of hundreds of millions of people in 60 days. That says something. You input a prompt and you get a response back. ChatGPT is super-intuitive. It’s a technology with potential for many killer use cases. AR/VR is promising technology with upside potential, but there’s still work that needs to be done to tie that technology to the use case. Virtual reality gaming is number one, for sure. But the path to leveraging that technology to enhance how we operate other stuff still needs more clarity. That said, we recently published a white paper talking about the build-outs around the globe, driven by the combination of public incentivies and private investments. Everywhere around the world, everybody wants to build up their manufacturing facilities. We conducted interviews with semiconductor experts, and touched on AR/VR when we asked what they did during COVID when the whole world shut down. Is AR/VR like a hammer looking for nails? The overall response we got was pretty positive. They said that AR/VR probably will be tremendously useful at some future date. But they like where the technologies are going. For example, there are constraints like heat dissipation and the size of the headset, but the belief is the technology will evolve. As it matures to become more user-centric, you might think about using an AR/VR device to control the operations of the equipment in a fab. But there is work needed from a value perspective — connectivity and processing, for example.

Karazuba: AR/VR in the past has largely been a victim of its own hype cycle. There’s a lot of promises people have made. We’ve spent a little bit of time with AR/VR folks. There’s certainly an acknowledgement that whatever success the Apple AR/VR headset has will largely set the tone for the next half decade for what the AR/VR market is. These folks are not undeterred by that. Are we at a point today where you can walk around all day with mixed reality? No. With a home gaming system, being tied to the wall is probably a small price to pay for the constant AC power and the performance advantages that will provide. This is going to take some time. The value proposition is there, but the timing may not be right today. We saw this with the watch and wearables. Now, everybody has one of these. But it took five to seven years before it really took off.

Vora: We’ve worn watches for decades, so it’s not something new. It’s just that what we wear now is different. But with AR/VR, we’ve never done that before. How do you suddenly expect massive change like that?

Karazuba: But most of us are wearing eyeglasses. If you have a form factor that is a version of what we have now, where information is just simply overlaid on what we’re seeing, it’s not that far of a jump for mixed reality or augmented reality. However, with virtual reality, I find it hard to believe that people are going to walk into a conference room with a bunch of other people and put a headset on.

Yanamadala: We’ve seen devices and sensors deployed practically everywhere. Platforms that offer high-performance computing, along with secure, power-efficient hardware and connectivity are available today, and they will make this trend possible. But untethered or ambient consumer experiences in the mass market will have their challenges. We will need to invest in substantial infrastructure to enable technology to operate invisibly in the background. So while consumer-facing technology deployments increasingly become untethered, the compute and connectivity infrastructure will still require connections for power and bandwidth.

SE: People have been sounding the alarm for hardware security for years, but with limited success. What’s changed today is that we have many more connected devices and more valuable data. Is the chip industry starting to take this seriously? Or is the problem now so immense and pervasive that anything we do is just going to be a drop in the bucket?

Yanamada: Security is fundamental from the chip level, and five years ago we saw an opportunity to proactively improve the quality of chip security. IoT was in its early stages, and each chip vendor had varied and fragmented approaches to security. They also rarely approached an independent evaluation lab to check the robustness of their security implementation. But with increasing connectivity and data becoming more valuable, hackers were paying close attention, and governments were considering what action to take to protect consumers. That’s why in 2019, we launched PSA Certified – to rally the ecosystem to be proactive with security best practices. It’s critically important that chip vendors, software platforms, OEMs, and CSPs can deploy and access standardized Root of Trust services. Security is complicated. You need the whole value chain to work together.

Vora: Security architectures, at least on the hardware side, have come a long way. We pretty much now have a semiconductor TPM-like [Trusted Platform Module] capability, with security capabilities built into even small microcontrollers. They have cryptographic engines, randomizers, and all sorts of security elements built in. The fundamental challenge with security is that just putting some security features on a chip and providing all the technology pieces won’t solve the security challenge. Security is more of a system challenge and a policy challenge. In many cases, people have to think about it within the context of the entire network. And then, it’s only as strong as the weakest link in the network. That piece of security is going to grow in complexity as we start seeing more complex use cases with AI coming into play with IoT. On the other side, though, as data handling of AI moves closer to the edge, we will start seeing more local inferencing and local data being worked on without the need to mindlessly transport data across layers of networks and across the cloud. We’re going to see some lower risk and improvements from a data-in-flight perspective, because of a lot of more localization of intelligence and compute happening at different layers of the edge. As we start moving more to the edge, AI starts getting more of a hold there. But as a whole, security will remain a challenge. The fundamental challenges with security have not changed. It’s just the context and the systems in which we will have to apply them are different.

Karazuba: The semiconductor industry is finally starting to understand the true nature of what security breaches could mean with the type of data we’re handling. Security is a day zero responsibility of anyone building a product, whether that product is a chip or a device, and security responsibilities proliferate across the entire lifecycle of the of any device, from the person who is architecting the chip, to the person designing the smartphone, to the carrier. I would argue that carrier responsibilities for security go as far as the stopping those robo calls that we all get, and the spam calls and phishing calls. The internet service providers have a responsibility to stop the phishing e-mails. That’s all part of security. Obviously, with banks and financial institutions, their security is generally pretty good. But it stretches the entire way, and in the security world, the weakest link is always the security profile of your device. We’re getting better. We always could be better. But I am more encouraged now than I’ve been at any point since I really started looking at security of devices. I’m more encouraged by the way chips are being designed, deployed, manufactured, and delivered to customers.

Kurniawan: There’s some certification for IoT devices before those are sent into the market to make sure there is some security standard they adhere to. But two key words I mentioned before, collaboration and flexibility, are applicable to security, as well. Collaboration involves where you see the rest of the system, including other components in the technology set, going to evolve in the future. And flexibility is required, because security is a moving target. It needs to evolve because as you upgrade your system, your software, a vulnerability will move, as well. You need flexibility and security-minded thinking infused into your chip design.

Related Reading
Preparing For An AI-Driven Future In Chips (part 1 of above roundtable)
Designs need to be flexible enough to handle an onslaught of continuous and rapid changes, but secure enough to protect data.

The post Broad Impact For Accelerating Tech Cycles appeared first on Semiconductor Engineering.