An AI accelerator is a high-performance parallel computation engine that is especially built for the efficient processing of AI workloads such as neural networks.
Traditionally, computer scientists concentrated on inventing algorithmic techniques that were tailored to individual problems and implemented in a high-level procedural language. Some algorithms could be threaded to take use of available hardware; nevertheless, enormous parallelism was difficult to achieve due to the consequences of Amdahl’s Law.
How AI Accelerator works?
There are now two different AI accelerator markets: data centers and edge computing.
Massively scalable computational architectures are required for data centers, notably hyperscale data centers. The semiconductor industry is investing heavily in this area. Cerebras, for example, invented the Wafer-Scale Engine (WSE), the world’s largest chip for deep-learning systems. The WSE, by providing additional computing, memory, and network capacity, can facilitate AI research at far quicker rates and scalability than traditional systems.
The other end of the spectrum is represented by the edge. Because intelligence is spread at the network’s edge rather than a more centralized position, energy efficiency is critical, and real estate is restricted. AI accelerator IP is embedded into edge SoC devices, regardless of size.
AI accelerator IP is embedded into edge SoC devices, which, no matter how tiny, provide the near-instantaneous results required for, instance, interactive apps on smartphones or industrial robots.
Different Types of Hardware AI Accelerators
While the WSE is one method for speeding AI applications, there are various forms of hardware AI accelerators for applications that do not require a single big chip. Here are several examples:
- Graphics processing units (GPUs)
- Massively multicore scalar processors
- Spatial accelerators, such as Google’s Tensor Processing Unit (TPU)
Each of these are individual chips that may be joined in tens to hundreds to form bigger devices capable of processing massive neural networks. Coarse-grain reconfigurable architectures (CGRA) are gaining traction in this field because they can provide appealing tradeoffs between performance and energy efficiency on one hand and flexibility to design diverse networks on the other.
Benefits of an AI Accelerator
Given the importance of processing speed and scalability in AI applications, AI accelerators play a significant role in delivering the near-instantaneous results that make these applications attractive. Let’s take a closer look at the main advantages of AI accelerators:
Energy conservation – AI accelerators can outperform general-purpose computation computers by 100-1,000 times. AI accelerators can’t afford to use too much power or dissipate too much heat while conducting massive quantities of computations, whether they’re employed in a data center setting that has to be kept cool or an edge application with a limited power budget.
Computational speed and latency – AI accelerators reduce the latency of the time it takes to generate a response due to their speed. This low latency is vital in safety-sensitive applications such as advanced driver assistance systems (ADAS), where every second counts.
Scalability – It is difficult to write an algorithm to solve a problem. It is significantly more difficult to parallelize this technique over several cores for increased processing capabilities. However, in the field of neural networks, AI accelerators provide a degree of performance speed acceleration that is almost comparable to the number of cores involved.
Architecture that is heterogeneous – This method enables a given system to accommodate many specialized processors to handle specific activities, hence delivering the computational performance required by AI applications. It may also use other devices for calculations, such as magnetic and capacitive characteristics of different silicon architectures, memory, and even light.