Defining the modern architecture of today’s technological advancements in electronics components, Artificial Intelligence (AI) has paved a new road for inferring a variety of insights while utilizing its different models. So is the new term AI Accelerators.
AI brings a fresh air of advanced algorithms that drive the human-machine feedback without any human intervention.
This led to better computational speed as well as brings accurate insights based on multiple data evaluations under strict AI models.
With specialized hardware for AI workloads growing exponentially in the last decade, complex AI tasks with data-intensive workload have led to the introduction of AI Accelerators that possesses a dedicated processor designed to accelerate machine learning computations. In this article we will introduce What are AI Accelerators and How does it Work?
According to this, implementing AI algorithms executed in software on CPUs and computer hardware will need more power thus, making them impractical for many the AI inference applications.
However, before advancing towards the benefits of AI Accelerators in detail, let’s infer What are AI Accelerators and How does it Work?
Basic Understanding of AI Accelerators
Considering the first question of What are AI accelerators, the first thing we should pick up from the name is that it’s a model used to accelerate the rate of the computational speed of a system or hardware as the name suggests.
In other words, an AI accelerator belongs to a category of specialized hardware accelerator or can be an automatic data processing system specially designed to accelerate computer science applications, artificial neural networks, machine visualization and even machine learning.
Grasping the concept of types of AI Accelerators used above, we can categorize AI accelerator into software and hardware types.
Software AI accelerators are specifically designed to bring in fine AI performance improvements through their software optimizations to make platforms up to 100x faster. Also, depending on the requirements, the need for AI acceleration at the edge or in the cloud can vary too.
On the other hand, hardware AI Accelerator are silicon optimized that can speed up common processes used by AI models.
One of the first AI hardware accelerator normally used were graphics processing units (GPUs), that were built to render 3D images inside games.
However, right now we have a different set of some of the popular Hardware AI Accelerator on the market that includes, Vision Processing Unit (VPU), Field-Programmable Gate Array (FPGA), Application-Specific Integrated Circuit (ASIC), and Tensor Processing Unit (TPU).
Comparing Software AI Accelerators with Hardware AI Accelerators
As the diversity of AI workloads increases, the business demand for a variety of AI-optimized hardware architectures also rises as we classify them into three main categories like AI-accelerated CPU, dedicated hardware AI Accelerator, and AI-accelerated GPU.
Though AI hardware has continued to take tremendous achievements with its variety of models present in the market, the growth rate of AI model complexity has slowly surpassed its hardware advancements.
Therefore, to solve the increasing complexity of advancements, AI engineers have now started to rely more on performance enhancements driven by software AI Accelerator.
To sum it up in layman’s language, if hardware acceleration is like upgrading your bike to have the latest features, software acceleration is more like having a complete re-envisioned mode of travel such as a supersonic jet.
In short, all of it depends on the type of AI workload the business intends to handle according to its need.
How do AI Accelerators Work?
In general, the main aim of any AI accelerator is to analyze algorithms faster while using minimal power.
AI accelerators are developed to take an algorithmic approach to match specific tasks for dedicated problems.
Also, the location of AI Accelerator and compute architecture is the key to processing its functionality.
Some of the most significant points at which AI Accelerators are generally placed range from data centers and the edge.
How to Choose the Right AI Accelerators?
Specifically concentrating on hardware AI Accelerators here, since not every AI accelerated hardware is best for every AI-based application, several factors must be considered before choosing the right AI accelerator for a business application and its specific requirements.
The first parameter that should be considered in these cases is the model type and programmability of the AI Accelerator, wherein the customer needs to analyze what kind of model size, supported frameworks and custom operators are required for that particular application.
Another factor that needs to be considered is the target throughput, latency, and the overall costs including the price and performance ratio.
Last but not least crucial factor in choosing the right AI accelerator is the type of compiler and runtime toolchain one can choose while leveraging it easily.
As the hardware AI Accelerator become popular, their need will depend on the type of application they are best suitable for that will decide their fate in the next few years.
In short, to help accommodate market demands on AI systems, engineers are now utilizing AI Accelerators more to ensure that modern technology can achieve real-time data.