AI accelerators – what are they and why do you need them?

AI accelerators – what are they and why do you need them?

AI or artificial intelligence is a technology that has recently taken the world by storm. From personalising customer experience to providing better inventory planning, the scope of AI integration for businesses is endless. But while talking about AI implementation most people merely focus on the technologies and not the hardware aspects. When it comes to hardware to support advanced AI implementations, AI accelerators are very important.

What is an artificial intelligence accelerator?

As evident from the name itself, this component is intended to accelerate AI implantations. Workloads can be unpredictable and there can be a constant increase in dataset volumes in AI integrations. So, an accelerator would help support this expanding scope of processing. An artificial intelligence accelerator is a computational device that supports a host of AI applications like machine learning and others.

The real difference of using an accelerator can be felt after final implementation of the machine learning API in your application. Some of them involve slight delays involved in the data processing in the background and the eventual decision making. AI-based predictions can be heavy on the computational devices. An accelerator would act as a reliable support to such systems. By sharing the computational load, an accelerator significantly reduces the processing times of AI systems.

Accelerator types for an AI system

Depending on the actual purpose of AI implementation the type of accelerator used varies.

  • For systems that require deep learning capabilities tensor processing unit or TPU is a very popular accelerator. For scalability and cost effectiveness, cloud TPUs are very commonly used. These are preferred as they are much faster than some of the best GPUs out there. Edge TPUs are also used in cases where a small-scale custom integration is required.
  • GPU or graphics processing units are the other common accelerator options for AI systems. For image processing applications a good GPU supports the machine learning algorithm integrated.
  • While talking about image processing, machine vision is an emerging technology and VPU or vision processing unit is the type of accelerator used in this case. This helps processing video based datasets with better accuracy and enhanced speeds.
  • For applications that might need continuous personalizations in algorithms, FPGA or field programmable gate arrays are very useful. This type of accelerator can be used for the new frameworks where post-manufacturing reconfiguration is essential. The freedom to fully personalized the functionality is one main reason why some choose FPGA over GPU.
  • To take the capabilities of the FPGAs one step ahead, there are ASICs or application specific integrated circuits. Function parallelisation becomes possible in large server integrations with the use of ASICs as accelerators. Businesses choose to add accelerators for numerous reasons. If your core focus is speed enhancement, you might find ASICs to be beneficial.

It becomes easy to choose the relevant accelerator type when you are clear about the design requirements. Consider the programmability aspects of the accelerator and needs of the framework to make a better decision. Budget considerations should also be part of your decision making strategy.

Perks of using an AI accelerator                                                        

AI accelerators or any type of additional hardware in the AI system involve an extra expense. An accelerator brings the following benefits that make it worth the additional expenditure.

  • Energy efficient systems become a reality

When there is heavy computation involved, and longer processing times, it automatically increases the energy consumption. An accelerator reduces the latency and avoids delays in processing. The time saved here would be the same as money saved.

  • System size customization

By using a scalable accelerator framework you make it possible to optimize the system size in the long run. With this you can expand the functionality and get better predictions without considerable increase in the framework size.

  • Better speeds

In some places, accurate predictions are enough. In other places, quicker real time data processing is important. If the decision should be fast and if this determines another timely action in the workflow, an accelerator can be useful. This removes latency in the processes.

Even the best hardware sometimes turns sluggish when you try to load heavy machine learning algorithms. So, do not underestimate the power of good AI accelerators. If you have planned an AI-based architecture, consider investing in relevant AI accelerators. This will help in creating a more robust system for your organization.

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