AI Accelerators

- Overview

The use of artificial intelligence (AI) has dramatically transformed various industries around the world, resulting in an increasing demand for AI processing power. 

The rapid development of AI has led to the rise of AI accelerators, which are highly specialized hardware designed to accelerate AI applications. AI accelerators are high-performance parallel computers designed to efficiently process AI workloads such as neural networks. 

Traditionally, in software design, computer scientists have focused on developing algorithmic methods that fit specific problems and implementing them in high-level procedural languages. 

In order to take advantage of available hardware, some algorithms can be threaded; however, due to the effects of Amdahl's law, massive parallelism is difficult to achieve.

As intelligence moves to the edge in many applications, AI accelerators are creating greater differentiation. The edge offers a wide variety of applications that require AI accelerators to be specifically optimized for different characteristics such as latency, energy efficiency, and memory based on the needs of the end application. 

For example, while autonomous navigation requires computational response latencies limited to 20μs, voice and video assistants must understand spoken keywords within 10μs and gestures within hundreds of milliseconds.

In the future, cognitive systems designed to simulate human thinking processes will become even more prominent. Compared to today's neural networks, cognitive systems have a deeper understanding of how to interpret data at different levels of abstraction.

- Two Distinct AI Accelerator Spaces

There are currently two distinct AI accelerator spaces: the data center and the edge.

Data centers, especially hyperscale data centers, require massively scalable computing architectures. For this field, the chip industry is becoming bigger and stronger. For example, Cerebras pioneered the Wafer Scale Engine (WSE), the largest chip ever built for deep learning systems. By providing more computing, memory and communication bandwidth, WSE can support artificial intelligence research with greater speed and scalability than traditional architectures.

The edge represents the other end of the spectrum. Here, energy efficiency is key and space is limited because intelligence is distributed at the edge of the network rather than in more centralized locations. AI accelerator IP is integrated into edge SoC devices, no matter how small, to deliver the near-instant results required for interactive programs running on smartphones or industrial robots. 

- Different Types of Hardware AI Accelerators

The Wafer Scale Engine (WSE) is an AI chip and accelerator created by Cerebras Systems, an AI supercomputer firm based in California. The WSE is the world's largest computer chip, and the third generation, WSE-3, is considered the fastest AI processor in the world. 

While WSE is one way to accelerate AI applications, there are several other types of hardware AI accelerators for applications that don’t require large dies. 

Examples include:

• Graphics processing unit (GPU)
• Large-scale multi-core scalar processor
• Spatial accelerators, such as Google's Tensor Processing Unit (TPU)

Each is an independent chip, and dozens to hundreds can be combined into larger systems to handle large neural networks. 

Coarse-grained reconfigurable architectures (CGRA) have gained significant momentum in this area, as they can offer an attractive trade-off between performance and energy efficiency on the one hand, and the flexibility to adapt to different networks on the other hand. 

Different AI accelerator architectures may offer different performance tradeoffs, but they all require an associated software stack to achieve system-level performance; otherwise, the hardware may not be fully utilized. 

To facilitate the connection between high-level software frameworks such as TensorFlow or PyTorch and different AI accelerators, machine learning compilers are emerging to enable interoperability. A representative example is the Facebook Glow compiler.

- The Benefits of AI Accelerators

An AI accelerator, also known as an AI chip, deep learning processor or neural processing unit (NPU), is a hardware accelerator that is built to speed AI neural networks, deep learning and machine learning.

Given that processing speed and scalability are two key demands from AI applications, AI accelerators play a critical role in delivering the near-instantaneous results that make these applications valuable. 

Let’s dive into the top benefits of AI accelerators in some more detail:

• Speed: AI accelerators can significantly increase the processing speed of AI algorithms, which can be critical for time-sensitive tasks. For example, AI accelerators can help advanced driver assistance systems (ADAS) respond faster, which is important for safety.
• Energy efficiency: AI accelerators can reduce the power consumption of AI applications, which can be important for battery-powered devices or applications that need to run for long periods of time.
• Scalability: AI accelerators can help AI computations become more scalable.
• Parallel processing: AI accelerators can use parallel processing to speed up processes in neural networks, which can optimize the performance of AI applications like generative AI and chatbots.
• Computational power: AI accelerators provide the computational power needed to advance AI technology. They can help AI systems handle complex tasks like image and speech recognition, natural language processing, and autonomous vehicles operation.
• Heterogeneous architecture: This approach allows a particular system to accommodate multiple specialized processors to support specific tasks, providing the computational performance that AI applications demand. It can also take advantage of different devices, for example, magnetic and capacitive properties of different silicon structures, memory, and even light for computations.

Here are some examples of how AI accelerators are being used:

• Autonomous vehicles. AI accelerators can capture and process data in near real time, making them critical to the development of self-driving cars, drones and other autonomous vehicles.
• Edge computing and edge AI.
• Large language models.
• Robotics.

[More to come ...]