Types of AI chips and accelerator chips

The demand for artificial Intelligence (AI) chips is increasing due to their suitability in certain AI applications. This development mirrors the rise of Graphic Processing Units (GPUs) for 3D applications years ago. However, traditional CPUs and even GPUs often fall short in handling the unique demands of AI workloads, prompting the emergence of AI-specific chips.

These chips offer low-precision arithmetic and high-level parallel computing at the hardware level, enabling them to execute AI computations quickly and efficiently. The term “AI chips” encompasses a range of semiconductor devices, with the most specialized among them referred to as AI accelerators, designed explicitly for accelerating AI tasks.

AI chips can be categorized in several ways, making selecting the ideal one for a specific application easier. In this article, we’ll explore different classes of AI chips and how this classification can be useful.

Classification

AI chips can be sorted based on their architecture, network topology, the AI workloads they handle, applications, frameworks, and power efficiency.

AI chips by architecture

One common way to categorize AI chips is by their architecture. Based on chip design, AI chips fall into one of several categories.

Central processing units (CPUs): Some CPUs have been adapted for AI workloads. While they use the traditional Von Neumann Architecture, these CPUs are optimized for AI applications by incorporating features like on-chip memory, integrated AI accelerators such as Neural Processing Units (NPUs), Vector Processing Units (which perform the same operation on multiple data elements simultaneously), specialized instructions like matrix multiplication, and optimized software and compilers for AI tasks. However, most AI-optimized CPUs still lag behind state-of-the-art AI chips in terms of performance and power efficiency.

Graphics processing units (GPUs): Originally designed for graphics applications, GPUs became the go-to choice for deploying enterprise AI applications. With their parallel computing architecture and ability to efficiently handle matrix operations, GPUs are widely used for training AI models. They excel in parallel processing and offer high throughput, making them ideal for tasks like deep learning, which require quickly processing large volumes of data. AI systems often use multiple interconnected GPUs for model training. GPUs also offer lower latency due to faster cache data access speeds and excel in floating-point processing compared to CPUs.

Field-programmable gate arrays (FPGAs): FPGAs are often used to deploy trained AI models because of their flexibility and reconfigurability. They can be customized for various tasks, making them ideal for AI applications requiring frequent updates or specialized algorithms. Their ability to adapt to specific AI processes allows them to be hyper-specialized, offering lower latency, reduced power consumption, and cost advantages compared to CPUs or GPUs. For specific AI tasks, FPGAs can also be more energy-efficient than GPUs.

Application-specific integrated circuits (ASICs): ASICs are custom-designed chips built for specific tasks, such as image recognition or natural language processing. They deliver the highest performance and energy efficiency for their intended purpose but are costly and complex to develop. Often implemented based on FPGAs during the design phase, ASICs are fine-tuned for training, inference, or both. While they lack flexibility, ASICs are the most efficient solution once a design reaches production.

Neural processing units (NPUs): NPUs are designed to accelerate neural network computations, combining hardware and software for AI tasks. They feature dedicated Matrix Multiplication Units (MMUs) optimized for matrix operations, which are fundamental to deep learning. Many NPUs include hardware support for activation functions like ReLU, sigmoid, and tanh, enhancing the efficiency of non-linear transformations in neural networks.

NPUs often include large on-chip memories for storing weights and activations and leverage specialized memory architectures, such as tensor cores or systolic arrays, to handle data-intensive operations. Unlike GPUs, NPUs offer finer-grained parallelism, enabling better hardware utilization. Some are also optimized for sparse matrices, common in specific neural networks. Advanced power efficiency techniques, such as dynamic voltage and frequency scaling, enhance their performance.

Tensor processing units (TPUs): TPUs, developed by Google, are a type of NPU specifically built to accelerate TensorFlow-based deep learning models. They use innovative architectures like systolic arrays to efficiently manage matrix multiplications and other deep learning operations. TPUs are primarily offered through Google Cloud services, making them tightly integrated with Google’s AI/ML ecosystem. Designed for high-performance, task-specific operations, TPUs are a cornerstone of Google’s infrastructure for advanced AI applications.

Neuromorphic chips: Neuromorphic chips are specialized NPUs inspired by the structure and function of the human brain. These chips use “spiking neurons” that communicate through discrete electrical pulses or spikes. The spikes travel across “synaptic connections,” which can strengthen or weaken over time, mimicking the learning processes of biological brains. Unlike traditional chips that operate on a clock signal, neuromorphic chips are event-driven, processing information only when spikes are received, resulting in exceptional energy efficiency.

Computation is distributed across interconnected neurons, enabling massive parallel processing. Memory and processing units are integrated within the same physical space, reducing data movement and saving energy. Some neuromorphic chips also use analog or mixed-signal circuits to replicate the behavior of biological neurons closely and synapses closely, enhancing energy efficiency and performance for specific tasks.

AI chips by topology

Systolic arrays: These chips are designed with a highly regular, structured architecture where data flows rhythmically through a grid of processing elements. This design is ideal for matrix operations, minimizing data movement and improving efficiency. Its regularity allows for highly efficient hardware implementation.

Spatial architectures: This design leverages spatial parallelism, enabling operations to be performed simultaneously across multiple processing elements. It’s particularly effective for achieving significant speedups in highly parallel operations and can be adapted to various AI workloads. However, spatial architectures are more complex and challenging to implement than systolic arrays.

Hybrid architectures: This approach combines features of systolic arrays and spatial architectures. It capitalizes on the strengths of each — offering high throughput, data locality, and flexibility. The primary advantage of the hybrid model is its ability to be optimized for specific AI models and operations, making it a versatile choice for various tasks.

AI chips by workload

AI chips can be classified based on the specific workloads they are designed to handle:

Machine learning chips: These chips support several machine learning algorithms, including deep learning and traditional methods such as vector machines and decision trees. They’re typically FPGAs or CPUs with specialized AI instructions. Machine learning chips offer more general-purpose capabilities compared to other AI chip types.

Deep learning chips: Primarily optimized for deep learning tasks such as image recognition, natural language processing, and object detection, these chips include GPUs (e.g., NVIDIA’s Tensor Cores), TPUs (Google’s Tensor Processing Units), and specialized accelerators from companies like Graphcore and Cerebras. They’re characterized by high throughput, extensive parallelism, and optimizations for matrix operations central to neural networks.

Edge AI chips: Designed for low-power applications at the edge, such as smartphones and IoT devices, these chips include specialized AI accelerators from companies like Qualcomm, Ambarella, and Edge Impulse. They’re known for low power consumption, compact form factors, and integration with other sensors and processors.

Computer vision chips: These AI chips are tailored for image recognition, object detection, video analysis, and computer vision applications. Examples include chips from Mobileye (used in autonomous driving systems), Intel Movidius, and specialized vision processors in security cameras. They are optimized for real-time applications that require low latency.

Natural language processing (NLP) chips: Designed for accelerating NLP tasks such as speech recognition, machine translation, and sentiment analysis, these chips are optimized for sequence processing, recurrent neural networks (RNNs), and transformer models.

It’s important to note that AI chip categorization based on workloads is not always mutually exclusive. Many chips overlap in functionality and can effectively handle multiple AI workloads.

AI chips by applications

Training chips: Built for the computationally intensive AI model training process, they offer high throughput for processing massive datasets quickly and feature large memory bandwidth to manage the heavy data flow during training. Unlike inference chips, training chips require higher precision calculations. Examples include GPUs like NVIDIA’s A100 and H100, Google’s TPUs, and specialized training accelerators from companies such as Cerebras and Graphcore.

Inference chips: These chips are optimized for deploying trained AI models in real-world applications. They’re designed with low power consumption, making them suitable for edge devices and mobile applications. Inference chips are characterized by high throughput and low latency. Examples include FPGAs, mobile AI processors, edge TPUs, and computer vision ASICs. Unlike training chips, which prioritize performance, inference chips focus on power efficiency and latency.

Unified architectures: Some chips are designed to handle inference and training, offering versatility and cost-efficiency. These unified architectures streamline handling both applications within a single chip design.

AI-specific chips: They feature specialized architectures tailored to particular AI workloads and applications, often implemented as ASICs for maximum optimization.

AI chips by frameworks

Framework-specific chips: These are optimized for compatibility with specific AI frameworks, such as TensorFlow or PyTorch. Their tight integration with a framework ensures peak performance and efficiency but may limit flexibility with other frameworks. Examples include Google TPUs (TensorFlow), NVIDIA Jetson AGX Orin (TensorFlow, PyTorch, and others), Qualcomm Hexagon DSP (TensorFlow Lite and PyTorch Mobile), Graphcore IPU (with its own SDK), and Cambricon MLU200 Series (TensorFlow and PyTorch).

Framework-agnostic chips: They’re designed to work seamlessly with various AI frameworks, offering greater flexibility for developers. They can be used across different tools and libraries, though they may not provide the same optimization level as framework-specific chips. Examples include Xilinx FPGAs, NVIDIA GPUs with CUDA support, Google TPUs, and Graphcore IPUs.

AI chips with SDKs and libraries: Many chip manufacturers provide software development kits (SDKs) and libraries to streamline integration with popular AI frameworks. For instance, NVIDIA’s CUDA toolkit includes libraries and tools for developing and deploying AI applications on NVIDIA GPUs. Similarly, Intel’s OpenVINO toolkit offers optimization tools for deploying deep learning models on Intel hardware.

AI chips by power efficiency

High-power chips: These chips prioritize peak performance, often at the expense of power efficiency. They’re primarily used to train large-scale AI models in data centers or perform High-Performance Computing (HPC) tasks at the edge. Examples include high-end GPUs like NVIDIA’s A100 and H100 and Google’s TPU v4.

Power-efficient chips: They focus on reducing power consumption while maintaining adequate performance. They are ideal for mobile devices like smartphones and tablets, edge devices like wearables and IoT systems, and autonomous vehicles. Many are also employed in data centers for AI inference tasks. Examples include mobile AI processors, Google Edge TPUs, FPGAs, and neuromorphic chips.

Ultra-low-power chips: These chips are optimized for extreme energy efficiency and are used in applications such as TinyML, wireless sensor networks, and implantable medical devices. Examples include specialized TinyML chips and neuromorphic chips.

Hybrid AI chips: Hybrid AI chips represent a design philosophy rather than a single type of chip, integrating multiple processing units or architectures onto a single chip. They combine the capabilities of various components, such as CPU cores for general-purpose tasks, GPU cores for parallel computations like deep learning, specialized AI accelerators for operations such as matrix multiplication and convolutions, and FPGAs for flexibility in customizing AI workloads.

By blending the strengths of these different units, hybrid chips optimize performance and energy efficiency for specific AI tasks. This approach allows for greater flexibility in managing diverse AI workloads by dynamically switching between processing units. Offloading computationally intensive tasks to specialized accelerators reduces latency and improves real-time performance.

Hybrid AI chips are particularly valuable in autonomous vehicles and edge AI applications. For example, in edge devices, a hybrid chip might pair a low-power CPU with a dedicated AI accelerator to handle tasks like voice recognition or anomaly detection. In autonomous vehicles, these chips could integrate CPU cores for general control, GPUs for image processing, and specialized accelerators for object detection.