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.

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.

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

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.

2013 was a great year for games, with groundbreaking titles that pushed the medium forward and helped define an era. There was the post-apocalyptic narrative adventure The Last of Us, which spawned a huge sequel and a hit TV show. There were exciting sequels, like the pirate RPG Assassin’s Creed IV: Black Flag, and…

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Generative AI is today’s buzziest form of artificial intelligence, and it’s what powers chatbots like ChatGPT, Ernie, LLaMA, Claude, and Command—as well as image generators like DALL-E 2, Stable Diffusion, Adobe Firefly, and Midjourney. Generative AI is the branch of AI that enables machines to learn patterns from vast datasets and then to autonomously produce new content based on those patterns. Although generative AI is fairly new, there are already many examples of models that can produce text, images, videos, and audio.

Many “foundation models” have been trained on enough data to be competent in a wide variety of tasks. For example, a large language model can generate essays, computer code, recipes, protein structures, jokes, medical diagnostic advice, and much more. It can also theoretically generate instructions for building a bomb or creating a bioweapon, though safeguards are supposed to prevent such types of misuse.

What’s the difference between AI, machine learning, and generative AI?

Artificial intelligence (AI) refers to a wide variety of computational approaches to mimicking human intelligence. Machine learning (ML) is a subset of AI; it focuses on algorithms that enable systems to learn from data and improve their performance. Before generative AI came along, most ML models learned from datasets to perform tasks such as classification or prediction. Generative AI is a specialized type of ML involving models that perform the task of generating new content, venturing into the realm of creativity.

What architectures do generative AI models use?

Generative models are built using a variety of neural network architectures—essentially the design and structure that defines how the model is organized and how information flows through it. Some of the most well-known architectures are variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers. It’s the transformer architecture, first shown in this seminal 2017 paper from Google, that powers today’s large language models. However, the transformer architecture is less suited for other types of generative AI, such as image and audio generation.

Autoencoders learn efficient representations of data through an encoder-decoder framework. The encoder compresses input data into a lower-dimensional space, known as the latent (or embedding) space, that preserves the most essential aspects of the data. A decoder can then use this compressed representation to reconstruct the original data. Once an autoencoder has been trained in this way, it can use novel inputs to generate what it considers the appropriate outputs. These models are often deployed in image-generation tools and have also found use in drug discovery, where they can be used to generate new molecules with desired properties.

With generative adversarial networks (GANs), the training involves a generator and a discriminator that can be considered adversaries. The generator strives to create realistic data, while the discriminator aims to distinguish between those generated outputs and real “ground truth” outputs. Every time the discriminator catches a generated output, the generator uses that feedback to try to improve the quality of its outputs. But the discriminator also receives feedback on its performance. This adversarial interplay results in the refinement of both components, leading to the generation of increasingly authentic-seeming content. GANs are best known for creating deepfakes but can also be used for more benign forms of image generation and many other applications.

The transformer is arguably the reigning champion of generative AI architectures for its ubiquity in today’s powerful large language models (LLMs). Its strength lies in its attention mechanism, which enables the model to focus on different parts of an input sequence while making predictions. In the case of language models, the input consists of strings of words that make up sentences, and the transformer predicts what words will come next (we’ll get into the details below). In addition, transformers can process all the elements of a sequence in parallel rather than marching through it from beginning to end, as earlier types of models did; this parallelization makes training faster and more efficient. When developers added vast datasets of text for transformer models to learn from, today’s remarkable chatbots emerged.

How do large language models work?

A transformer-based LLM is trained by giving it a vast dataset of text to learn from. The attention mechanism comes into play as it processes sentences and looks for patterns. By looking at all the words in a sentence at once, it gradually begins to understand which words are most commonly found together and which words are most important to the meaning of the sentence. It learns these things by trying to predict the next word in a sentence and comparing its guess to the ground truth. Its errors act as feedback signals that cause the model to adjust the weights it assigns to various words before it tries again.

These five LLMs vary greatly in size (given in parameters), and the larger models have better performance on a standard LLM benchmark test. IEEE Spectrum

To explain the training process in slightly more technical terms, the text in the training data is broken down into elements called tokens, which are words or pieces of words—but for simplicity’s sake, let’s say all tokens are words. As the model goes through the sentences in its training data and learns the relationships between tokens, it creates a list of numbers, called a vector, for each one. All the numbers in the vector represent various aspects of the word: its semantic meanings, its relationship to other words, its frequency of use, and so on. Similar words, like elegant and fancy, will have similar vectors and will also be near each other in the vector space. These vectors are called word embeddings. The parameters of an LLM include the weights associated with all the word embeddings and the attention mechanism. GPT-4, the OpenAI model that’s considered the current champion, is rumored to have more than 1 trillion parameters.

Given enough data and training time, the LLM begins to understand the subtleties of language. While much of the training involves looking at text sentence by sentence, the attention mechanism also captures relationships between words throughout a longer text sequence of many paragraphs. Once an LLM is trained and is ready for use, the attention mechanism is still in play. When the model is generating text in response to a prompt, it’s using its predictive powers to decide what the next word should be. When generating longer pieces of text, it predicts the next word in the context of all the words it has written so far; this function increases the coherence and continuity of its writing.

Why do large language models hallucinate?

You may have heard that LLMs sometimes “hallucinate.” That’s a polite way to say they make stuff up very convincingly. A model sometimes generates text that fits the context and is grammatically correct, yet the material is erroneous or nonsensical. This bad habit stems from LLMs training on vast troves of data drawn from the Internet, plenty of which is not factually accurate. Since the model is simply trying to predict the next word in a sequence based on what it has seen, it may generate plausible-sounding text that has no grounding in reality.

Why is generative AI controversial?

One source of controversy for generative AI is the provenance of its training data. Most AI companies that train large models to generate text, images, video, and audio have not been transparent about the content of their training datasets. Various leaks and experiments have revealed that those datasets include copyrighted material such as books, newspaper articles, and movies. A number of lawsuits are underway to determine whether use of copyrighted material for training AI systems constitutes fair use, or whether the AI companies need to pay the copyright holders for use of their material.

On a related note, many people are concerned that the widespread use of generative AI will take jobs away from creative humans who make art, music, written works, and so forth. People are also concerned that it could take jobs from humans who do a wide range of white-collar jobs, including translators, paralegals, customer-service representatives, and journalists. There have already been a few troubling layoffs, but it’s hard to say yet whether generative AI will be reliable enough for large-scale enterprise applications. (See above about hallucinations.)

Finally, there’s the danger that generative AI will be used to make bad stuff. And there are of course many categories of bad stuff it could theoretically be used for. Generative AI can be used for personalized scams and phishing attacks: For example, using “voice cloning,” scammers can copy the voice of a specific person and call the person’s family with a plea for help (and money). All formats of generative AI—text, audio, image, and video—can be used to generate misinformation by creating plausible-seeming representations of things that never happened, which is a particularly worrying possibility when it comes to elections. (Meanwhile, as IEEE Spectrum reported this week, the U.S. Federal Communications Commission has responded by outlawing AI-generated robocalls.) Image- and video-generating tools can be used to produce nonconsensual pornography, although the tools made by mainstream companies disallow such use. And chatbots can theoretically walk a would-be terrorist through the steps of making a bomb, nerve gas, and a host of other horrors. Although the big LLMs have safeguards to prevent such misuse, some hackers delight in circumventing those safeguards. What’s more, “uncensored” versions of open-source LLMs are out there.

Despite such potential problems, many people think that generative AI can also make people more productive and could be used as a tool to enable entirely new forms of creativity. We’ll likely see both disasters and creative flowerings and plenty else that we don’t expect. But knowing the basics of how these models work is increasingly crucial for tech-savvy people today. Because no matter how sophisticated these systems grow, it’s the humans’ job to keep them running, make the next ones better, and with any luck, help people out too.