Smaller models with fewer parameters put image generators and chatbots within reach of an embedded NPU implementation.
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.
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).
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.
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.
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.
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
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