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  • ✇Semiconductor Engineering
  • Fundamental Issues In Computer Vision Still UnresolvedKaren Heyman
    Given computer vision’s place as the cornerstone of an increasing number of applications from ADAS to medical diagnosis and robotics, it is critical that its weak points be mitigated, such as the ability to identify corner cases or if algorithms are trained on shallow datasets. While well-known bloopers are often the result of human decisions, there are also fundamental technical issues that require further research. “Computer vision” and “machine vision” were once used nearly interchangeably, w
     

Fundamental Issues In Computer Vision Still Unresolved

2. Květen 2024 v 09:08

Given computer vision’s place as the cornerstone of an increasing number of applications from ADAS to medical diagnosis and robotics, it is critical that its weak points be mitigated, such as the ability to identify corner cases or if algorithms are trained on shallow datasets. While well-known bloopers are often the result of human decisions, there are also fundamental technical issues that require further research.

“Computer vision” and “machine vision” were once used nearly interchangeably, with machine vision most often referring to the hardware embodiment of vision, such as in robots. Computer vision (CV), which started as the academic amalgam of neuroscience and AI research, has now become the dominant idea and preferred term.

“In today’s world, even the robotics people now call it computer vision,” said Jay Pathak, director, software development at Ansys. “The classical computer vision that used to happen outside of deep learning has been completely superseded. In terms of the success of AI, computer vision has a proven track record. Anytime self-driving is involved, any kind of robot that is doing work — its ability to perceive and take action — that’s all driven by deep learning.”

The original intent of CV was to replicate the power and versatility of human vision. Because vision is such a basic sense, the problem seemed like it would be far easier than higher-order cognitive challenges, like playing chess. Indeed, in the canonical anecdote about the field’s initial naïve optimism, Marvin Minsky, co-founder of the MIT AI Lab, having forgotten to include a visual system in a robot, assigned the task to undergraduates. But instead of being quick to solve, the problem consumed a generation of researchers.

Both academic and industry researchers work on problems that roughly can be split into three categories:

  • Image capture: The realm of digital cameras and sensors. It may use AI for refinements or it may rely on established software and hardware.
  • Image classification/detection: A subset of AI/ML that uses image datasets as training material to build models for visual recognition.
  • Image generation: The most recent work, which uses tools like LLMs to create novel images, and with the breakthrough demonstration of OpenAI’s Sora, even photorealistic videos.

Each one alone has spawned dozens of PhD dissertations and industry patents. Image classification/detection, the primary focus of this article, underlies ADAS, as well as many inspection applications.

The change from lab projects to everyday uses came as researchers switched from rules-based systems that simulated visual processing as a series of if/then statements (if red and round, then apple) to neural networks (NNs), in which computers learned to derive salient features by training on image datasets. NNs are basically layered graphs. The earliest model, 1943’s Perceptron, was a one-layer simulation of a biological neuron, which is one element in a vast network of interconnecting brain cells. Neurons have inputs (dendrites) and outputs (axons), driven by electrical and chemical signaling. The Perceptron and its descendant neural networks emulated the form but skipped the chemistry, instead focusing on electrical signals with algorithms that weighted input values. Over the decades, researchers refined different forms of neural nets with vastly increased inputs and layers, eventually becoming the deep learning networks that underlie the current advances in AI.

The most recent forms of these network models are convolutional neural networks (CNNs) and transformers. In highly simplified terms, the primary difference between them is that CNNs are very good at distinguishing local features, while transformers perceive a more globalized picture.

Thus, transformers are a natural evolution from CNNs and recurrent neural networks, as well as long short-term memory approaches (RNNs/LSTMs), according to Gordon Cooper, product marketing manager for Synopsys’ embedded vision processor family.

“You get more accuracy at the expense of more computations and parameters. More data movement, therefore more power,” said Cooper. “But there are cases where accuracy is the most important metric for a computer vision application. Pedestrian detection comes to mind. While some vision designs still will be well served with CNNs, some of our customers have determined they are moving completely to transformers. Ten years ago, some embedded vision applications that used DSPs moved to NNs, but there remains a need for both NNs and DSPs in a vision system. Developers still need a good handle on both technologies and are better served to find a vendor that can provide a combined solution.”

The emergence of CNN-based neural networks began supplanting traditional CV techniques for object detection and recognition.

“While first implemented using hardwired CNN accelerator hardware blocks, many of those CNN techniques then quickly migrated to programmable solutions on software-driven NPUs and GPNPUs,” said Aman Sikka, chief architect at Quadric.

Two parallel trends continue to reshape CV systems. “The first is that transformer networks for object detection and recognition, with greater accuracy and usability than their convolution-based predecessors, are beginning to leave the theoretical labs and enter production service in devices,” Sikka explained. “The second is that CV experts are reinventing the classical ISP functions with NN and transformer-based models that offer superior results. Thus, we’ve seen waves of ISP functionality migrating first from pure hardwired to C++ algorithmic form, and now into advanced ML network formats, with a modern design today in 2024 consisting of numerous machine-learning models working together.”

CV for inspection
While CV is well-known for its essential role in ADAS, another primary application is inspection. CV has helped detect everything from cancer tumors to manufacturing errors, or in the case of IBM’s productized research, critical flaws in the built environment. For example, a drone equipped with the IBM system could check if a bridge had cracks, a far safer and more precise way to perform visual inspection than having a human climb to dangerous heights.

By combining visual transformers with self-supervised learning, the annotation requirement is vastly reduced. In addition, the company has introduced a new process named “visual prompting,” where the AI can be taught to make the correct distinctions with limited supervision by using “in-context learning,” such as a scribble as a prompt. The optimal end result is that it should be able to respond to LLM-like prompts, such as “find all six-inch cracks.”

“Even if it makes mistakes and needs the help of human annotations, you’re doing far less labeling work than you would with traditional CNNs, where you’d have to do hundreds if not thousands of labels,” said Jayant Kalagnanam, director, AI applications at IBM Research.

Beware the humans
Ideally, domain-specific datasets should increase the accuracy of identification. They are often created by expanding on foundation models already trained on general datasets, such as ImageNet. Both types of datasets are subject to human and technical biases. Google’s infamous racial identification gaffes resulted from both technical issues and subsequent human overcorrections.

Meanwhile, IBM was working on infrastructure identification, and the company’s experience of getting its model to correctly identify cracks, including the problem of having too many images of one kind of defect, suggests a potential solution to the bias problem, which is to allow the inclusion of contradictory annotations.

“Everybody who is not a civil engineer can easily say what a crack is,” said Cristiano Malossi, IBM principal research scientist. “Surprisingly, when we discuss which crack has to be repaired with domain experts, the amount of disagreement is very high because they’re taking different considerations into account and, as a result, they come to different conclusions. For a model, this means if there’s ambiguity in the annotations, it may be because the annotations have been done by multiple people, which may actually have the advantage of introducing less bias.”

Fig.1 IBM’s Self-supervised learning model. Source: IBM

Fig. 1: IBM’s Self-supervised learning model. Source: IBM

Corner cases and other challenges to accuracy
The true image dataset is infinity, which in practical terms leaves most computer vision systems vulnerable to corner cases, potentially with fatal results, noted Alan Yuille, Bloomberg distinguished professor of cognitive science and computer science at Johns Hopkins University.

“So-called ‘corner cases’ are rare events that likely aren’t included in the dataset and may not even happen in everyday life,” said Yuille. “Unfortunately, all datasets have biases, and algorithms aren’t necessarily going to generalize to data that differs from the datasets they’re trained on. And one thing we have found with deep nets is if there is any bias in the dataset, the deep nets are wonderful at finding it and exploiting it.”

Thus, corner cases remain a problem to watch for. “A classic example is the idea of a baby in the road. If you’re training a car, you’re typically not going to have many examples of images with babies in the road, but you definitely want your car to stop if it sees a baby,” said Yuille. “If the companies are working in constrained domains, and they’re very careful about it, that’s not necessarily going to be a problem for them. But if the dataset is in any way biased, the algorithms may exploit the biases and corner cases, and may not be able to detect them, even if they may be of critical importance.”

This includes instances, such as real-world weather conditions, where an image may be partly occluded. “In academic cases, you could have algorithms that when evaluated on standard datasets like ImageNet are getting almost perfect results, but then you can give them an image which is occluded, for example, by a heavy rain,” he said. “In cases like that, the algorithms may fail to work, even if they work very well under normal weather conditions. A term for this is ‘out of domain.’ So you train in one domain and that may be cars in nice weather conditions, you test in out of domain, where there haven’t been many training images, and the algorithms would fail.”

The underlying reasons go back to the fundamental challenge of trying to replicate a human brain’s visual processing in a computer system.

“Objects are three-dimensional entities. Humans have this type of knowledge, and one reason for that is humans learn in a very different way than machine learning AI algorithms,” Yuille said. “Humans learn over a period of several years, where they don’t only see objects. They play with them, they touch them, they taste them, they throw them around.”

By contrast, current algorithms do not have that type of knowledge.

“They are trained as classifiers,” said Yuille. “They are trained to take images and output a class label — object one, object two, etc. They are not trained to estimate the 3D structure of objects. They have some sort of implicit knowledge of some aspects of 3D, but they don’t have it properly. That’s one reason why if you take some of those models, and you’ve contaminated the images in some way, the algorithms start degrading badly, because the vision community doesn’t have datasets of images with 3D ground truth. Only for humans, do we have datasets with 3D ground truth.”

Hardware implementation, challenges
The hardware side is becoming a bottleneck, as academics and industry work to resolve corner cases and create ever-more comprehensive and precise results. “The complexity of the operation behind the transformer is quadratic,“ said Malossi. “As a result, they don’t scale linearly with the size of the problem or the size of the model.“

While the situation might be improved with a more scalable iteration of transformers, for now progress has been stalled as the industry looks for more powerful hardware or any suitable hardware. “We’re at a point right now where progress in AI is actually being limited by the supply of silicon, which is why there’s so much demand, and tremendous growth in hardware companies delivering AI,” said Tony Chan Carusone, CTO of Alphawave Semi. “In the next year or two, you’re going to see more supply of these chips come online, which will fuel rapid progress, because that’s the only thing holding it back. The massive investments being made by hyperscalers is evidence about the backlogs in delivering silicon. People wouldn’t be lining up to write big checks unless there were very specific projects they had ready to run as soon as they get the silicon.”

As more AI silicon is developed, designers should think holistically about CV, since visual fidelity depends not only on sophisticated algorithms, but image capture by a chain of co-optimized hardware and software, according to Pulin Desai, group director of product marketing and management for Tensilica vision, radar, lidar, and communication DSPs at Cadence. “When you capture an image, you have to look at the full optical path. You may start with a camera, but you’ll likely also have radar and lidar, as well as different sensors. You have to ask questions like, ‘Do I have a good lens that can focus on the proper distance and capture the light? Can my sensor perform the DAC correctly? Will the light levels be accurate? Do I have enough dynamic range? Will noise cause the levels to shift?’ You have to have the right equipment and do a lot of pre-processing before you send what’s been captured to the AI. Remember, as you design, don’t think of it as a point solution. It’s an end-to-end solution. Every different system requires a different level of full path, starting from the lens to the sensor to the processing to the AI.”

One of the more important automotive CV applications is passenger monitoring, which can help reduce the tragedies of parents forgetting children who are strapped into child seats. But such systems depend on sensors, which can be challenged by noise to the point of being ineffective.

“You have to build a sensor so small it goes into your rearview mirror,” said Jayson Bethurem, vice president of marketing and business development at Flex Logix. “Then the issue becomes the conditions of your car. The car can have the sun shining right in your face, saturating everything, to the complete opposite, where it’s completely dark and the only light in the car is emitting off your dashboard. For that sensor to have that much dynamic range and the level of detail that it needs to have, that’s where noise creeps in, because you can’t build a sensor of that much dynamic range to be perfect. On the edges, or when it’s really dark or oversaturated bright, it’s losing quality. And those are sometimes the most dangerous times.”

Breaking into the black box
Finally, yet another serious concern for computer vision systems is the fact that they can’t be tested. Transformers, especially, are a notorious black box.

“We need to have algorithms that are more interpretable so that we can understand what’s going on inside them,” Yuille added. “AI will not be satisfactory till we move to a situation where we evaluate algorithms by being able to find the failure mode. In academia, and I hope companies are more careful, we test them on random samples. But if those random samples are biased in some way — and often they are — they may discount situations like the baby in the road, which don’t happen often. To find those issues, you’ve got to let your worst enemy test your algorithm and find the images that break it.”

Related Reading
Dealing With AI/ML Uncertainty
How neural network-based AI systems perform under the hood is currently unknown, but the industry is finding ways to live with a black box.

The post Fundamental Issues In Computer Vision Still Unresolved appeared first on Semiconductor Engineering.

  • ✇Semiconductor Engineering
  • Chip Industry Week In ReviewThe SE Staff
    By Adam Kovac, Gregory Haley, and Liz Allan. Cadence plans to acquire BETA CAE Systems for $1.24 billion, the latest volley in a race to sell multi-physics simulation and analysis across a broad set of customers with deep pockets. Cadence said the deal opens the door to structural analysis for the automotive, aerospace, industrial, and health care sectors. Under the terms of the agreement, 60% of the purchase would be paid in cash, and the remainder in stock. South Korea’s National Intelligence
     

Chip Industry Week In Review

8. Březen 2024 v 09:01

By Adam Kovac, Gregory Haley, and Liz Allan.

Cadence plans to acquire BETA CAE Systems for $1.24 billion, the latest volley in a race to sell multi-physics simulation and analysis across a broad set of customers with deep pockets. Cadence said the deal opens the door to structural analysis for the automotive, aerospace, industrial, and health care sectors. Under the terms of the agreement, 60% of the purchase would be paid in cash, and the remainder in stock.

South Korea’s National Intelligence Service reported that North Korea was targeting cyberattacks at domestic semiconductor equipment companies, using a “living off the land” approach, in which the attacker uses minimal malware to attack common applications installed on the server. That makes it more difficult to spot an attack. According to the government, “In December last year, Company A, and in February this year, Company B, had their configuration management server and security policy server hacked, respectively, and product design drawings and facility site photos were stolen.”

As the memory market goes, so goes the broader chip industry. Last quarter, and heading into early 2024, both markets began showing signs of sustainable growth. DRAM revenue jumped 29.6% in Q4 for a total of $17.46 billion. TrendForce attributed some of that to  new efforts to stockpile chips and strategic production control. NAND flash revenue was up 24.5% in Q4, with solid growth expected to continue into the first part of this year, according to TrendForce. Revenue for the sector topped $11.4 billion in Q4, and it’s expected to grow another 20% this quarter. SSD prices rebounded in Q4, as well, up 15% to $23.1 billion. Across the chip industry, sales grew 15.2% in January compared to the same period in 2023, according to the Semiconductor Industry Association (SIA). This is the largest increase since May 2022, and that trend is expected to continue throughout 2024 with double-digit growth compared to 2023.

Marvell said it is working with TSMC to develop a technology platform for the rapid deployment of analog, mixed-signal, and foundational IP. The company plans to sell both custom and commercial chiplets at 2nm.

The Dutch government is concerned that ASML, the only maker of EUV/high-NA EUV lithography equipment in the world, is considering leaving the Netherlands, according to De Telegraaf.

Quick links to more news:

Design and Power
Manufacturing and Test
Automotive and Batteries
Security
Pervasive Computing and AI
Events

Design and Power

AMD appears to have hit a roadblock with the U.S. Department of Commerce (DoC) over a new AI chip it designed for the Chinese market, as reported by Bloomberg. U.S. officials told the company the new chip is too powerful to be sold without a license.

JEDEC released its new memory standard as a free download on its website. The JESD239 Graphics Double Data Rate SGRAM can reach speeds of 192 GB/s and improve signal-to-noise ratio.

Accellera rolled out its IEEE Std. 1800‑2023 Standard for SystemVerilog—Unified Hardware Design, Specification, and Verification Language, which is now available for free download. The decision to offer it at no cost is due to Accellera’s participation in the IEEE GET Program, which was founded in 2010 with the intention of providing  open access to some standards. Accellera also announced it had approved for release the Verilog-AMS 2023 standard, which offers enhancements to analog constructs, dynamic tolerance for event control statements, and other upgrades.

Chiplets are a hot topic these days. Six industry experts discuss chiplet standards, interoperability, and the need for highly customized AI chiplets.

Optimizing EDA hardware for the cloud can shorten the time required for large and complex simulations, but not all workloads will benefit equally, and much more can be done to improve those that can.

Flex Logix is developing InferX DSP for use with existing EFLX eFPGA from 40nm to 7nm. InferX achieves about 30 times the DSP performance/mm² than eFPGA.

The number of challenges is growing in power semiconductors, just as it is in traditional chips. This tech talk looks at integrating power semiconductors with other devices, different packaging impacts, and how these devices will degrade over time.

Vultr announced it will use NVIDIA’s HGX H100 GPU clusters to expand its Seattle-based cloud data center. The company said the expansion, which will be powered by hydroelectricity, will make the facility one of the cleanest, most power efficient data centers in the country.

Amazon Web Services will expand its presence in Saudi Arabia, announcing a new $5.3 billion infrastructure region in the country that will launch in 2026. The new region will offer developers, entrepreneurs and companies access to healthcare, education and other services.

Google is teaming up with the Geneva Science and Diplomacy Anticipator (GESDA) to launch the XPRIZE Quantum Applications, with a $5 million in prizes for winners who can demonstrate ways to use quantum computing to solve real-world problems. Teams must submit a proposal that includes analysis of how long their algorithm would need to run before reaching a solution to a problem, such as improving drug development or designing new battery materials.

South Korea’s nepes corporation has turned to Siemens EDA for solutions in the development of advanced 3D-IC packages. The deal will see nepes incorporating several Siemens technologies, including the Calibre nmPlatform, Hyperlynx software and Xpedition Substrate Integrator software.

Siemens also formalized a partnership with Nuclei System Technology in which the pair of companies will work together on solution support for Nuclei’s RISC-V processor cores. The collaboration will allow clients to monitor CPU program execution in real-time via Nuclei’s RISC-V CPU Ips.

Keysight and ETS-Lindgren announced a breakthrough test solution for cellular devices using non-terrestrial networks. The solution is capable of measuring and validating the performance of both the transmitter and receiver of devices capable of supporting the network.

Nearly fifty companies raised $800 million for power electronics, data center interconnects, and more last month.

Manufacturing and Test

SEMI Europe issued a position statement to the European Union, warning against additional export controls or rules on foreign investment. SEMI argued that free trade partnerships are a better method for ensuring security than bans or restrictions.

Revenues for the top five wafer fab equipment manufacturers declined 1% YoY in 2023 to $93.5 billion, according to Counterpoint Research. The drop was attributed to weak spending on memory, inventory adjustments, and low demand in consumer electronics. The tide is changing, though.

Bruker closed two acquisitions. One involved Chemspeed Technologies, a Switzerland-based provider of automated laboratory R&D and QC workflow solutions. The second involved Phasefocus, an image processing company based in the UK.

A Swedish company, SCALINQ, released a commercially available large-scale packaging solution capable of controlling quantum devices with hundreds of qubits.

Solid Sands, a provider of testing and qualification technology for compilers and libraries, will partner with California-based Emprog to establish a representative presence in the U.S.

Automotive

Tesla halted production at its Brandenberg, Germany, gigafactory after an environmental activist group attacked an electricity pylon, reports the Guardian.

Stellantis will invest €5.6 billion (~$6.1B) in South America to support more than 40 new products, decarbonization technologies, and business opportunities.

The amount of data being collected, processed, and stored in vehicles is exploding, and so is the value of that data. That raises questions that are still not fully answered about how that data will be used, by whom, and how it will be secured.

While industry experts expect many benefits of V2X technology, technological and social hurdles to cross. But there is progress.

Infineon released its next-gen silicon carbide (SiC) MOSFET trench technology with 650V and 1,200V options improving stored energies and charges by up to 20%, ideal for power semiconductor applications such as photovoltaics, energy storage, DC EV charging, motor drives, and industrial power supplies.

Hyundai selected Ansys to supply structural simulation solutions for vehicle body system analysis, providing end-to-end, predictively accurate capabilities for virtual performance validation.

ION Mobility used the Siemens Xcelerator portfolio for styling, mechanical engineering, and electric battery pack development for its ION M1-S electric motorbike.

Ethernovia sampled a family of automotive PHY transceivers that scale from 10 Gbps to 1 Gbps over 15 meters of automotive cabling.

The California Public Utilities Commission (CPUC) approved Waymo’s plan to expand its driverless robotaxi services to Los Angeles and other cities near San Francisco, reports Reuters.

By 2027, next-gen battery EVs (BEVs) will on average be cheaper to produce than comparable gas-powered cars, reports Gartner. But the firm noted that average cost of EV accident repair will rise by 30%, and 15% of EV companies founded in the last decade will be acquired or bankrupt.

University of California San Diego (UCSD) researchers developed a cathode material for solid-state lithium-sulfur batteries that is electrically conductive and structurally healable.

ION Storage Systems announced its anodeless and compressionless solid-state batteries (SSBs) achieved 125 cycles with under 5% capacity degradation in performance. ION has been working with the U.S. Department of Defense (DoD) to test its SSB before expanding into markets such as EVs, energy storage, consumer electronics, and aerospace.

Security

Advanced process nodes and higher silicon densities are heightening DRAM’s susceptibility to Rowhammer attacks, as reduced cell spacing significantly decreases the hammer count needed for bit flips. A multi-layered, system-level approach is crucial to DRAM protection.

Researchers at Bar-Ilan University and Rafael Defense Systems proposed an analytical electromagnetic model for IC shielding against hardware attacks.

Keysight acquired the IP of Firmalyzer, whose firmware security analysis technology will be integrated into the Keysight IoT Security Assessment and Automotive Security solutions, providing analysis into what is happening inside the IoT device itself.

Flex Logix joined the Intel Foundry U.S. Military Aerospace Government (USMAG) Alliance, ensuring U.S. defense industrial base and government customers have access to the latest technology, enabling successful designs for mission critical programs.

The EU Council presidency and European Parliament reached a provisional agreement on a Cyber Solidarity Act and an amendment to the Cybersecurity Act (CSA) concerning managed security services.

The EU Agency for Cybersecurity (ENISA) and partners updated the compendium on elections cybersecurity in response to issues such as AI deep fakes, hacktivists-for-hire, the sophistication of threat actors, and the current geopolitical context.

The Cybersecurity and Infrastructure Security Agency (CISA) launched efforts to help secure the open source software ecosystem; updated its Public Safety Communications and Cyber Resiliency Toolkit; and issued other alerts including security advisories for VMware, Apple, and Cisco.

Pervasive Computing and AI

Johns Hopkins University engineers used natural language prompts and ChatGPT4 to produce detailed instructions to build a spiking neural network (SNN) chip. The neuromorphic accelerators could power real-time machine intelligence for next-gen embodied systems like autonomous vehicles and robots.

The global AI hardware market size was estimated at $53.71 billion in 2023, and is expected to reach about $473.53 billion by 2033, at a compound annual growth rate of 24.5%, reports Precedence Research.

National Institute of Standards and Technology (NIST) researchers and partners built compact chips capable of converting light into microwaves, which could improve navigation, communication, and radar systems.

Fig. 1: NIST researchers test a chip for converting light into microwave signals. Pictured is the chip, which is the fluorescent panel that looks like two tiny vinyl records. The gold box to the left of the chip is the semiconductor laser that emits light to the chip. Credit: K. Palubicki/NIST

The Indian government is investing 103 billion rupees ($1.25B) in AI projects, including computing infrastructure and large language models (LLMs).

Infineon is collaborating with Qt Group, bringing Qt’s graphics framework to Infineon’s graphics-enabled TRAVEO T2G cluster MCUs to optimize graphical user interface (GUI) development.

Keysight leveraged fourth-generation AMD EPYC CPUs to develop a new benchmarking methodology to test mobile and 5G private network performance. The method uses realistic traffic generation to uncover a CPU’s true power and scalability while observing bandwidth requirements.

The AI industry is pushing a nuclear power revival, reports NBC, and Amazon bought a nuclear-powered data center in Pennsylvania from Talen Energy for $650 million, according to WNEP.

Bank of America was awarded 644 patents in 2023 for technology including information security, AI, machine learning (ML), online and mobile banking, payments, data analytics, and augmented and virtual reality (AR/VR).

Mistral AI’s large language model, Mistral Large, became available in the Snowflake Data Cloud for customers to securely harness generative AI with their enterprise data.

China’s smartphone unit sales declined 7% year over year in the first six weeks of 2024, with Apple declining 24%, reports Counterpoint.

Shipments of LCD TV panels are expected to reach 55.8 million units in Q1 2024, a 5.3% quarter over quarter increase, reports TrendForce. And an estimated 5.8 billion LED lamps and luminaires are expected to reach the end of their lifespan in 2024, triggering a wave of secondary replacements and boosting total LED lighting demand to 13.4 billion units.

Korea Institute of Science and Technology (KIST) researchers mined high-purity gold from electrical and electronic waste.

The San Diego Supercomputer Center (SDSC) and the University of Utah launched a National Data Platform pilot project, aimed at making access to and use of scientific data open and equitable.

Events

Find upcoming chip industry events here, including:

Event Date Location
ISS Industry Strategy Symposium Europe Mar 6 – 8 Vienna, Austria
GSA International Semiconductor Conference Mar 13 – 14 London
Device Packaging Conference (DPC 2024) Mar 18 – 21 Fountain Hills, AZ
GOMACTech Mar 18 – 21 Charleston, South Carolina
SNUG Silicon Valley Mar 20 – 21 Santa Clara, CA
SEMICON China Mar 20 – 22 Shanghai
OFC: Optical Communications & Networking Mar 24 – 28 Virtual; San Diego, CA
DATE: Design, Automation and Test in Europe Conference Mar 25 – 27 Valencia, Spain
SEMI Therm Mar 25- 28 San Jose, CA
MemCon Mar 26 – 27 Silicon Valley
All Upcoming Events

Upcoming webinars are here.

Further Reading and Newsletters

Read the latest special reports and top stories, or check out the latest newsletters:

Systems and Design
Low Power-High Performance
Test, Measurement and Analytics
Manufacturing, Packaging and Materials
Automotive, Security and Pervasive Computing

The post Chip Industry Week In Review appeared first on Semiconductor Engineering.

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