In our latest video we explain why there is so much excitement around using FPGAs for AI.
FPGAs have been around for decades but there has been renewed excitement around them thanks to AI, but why are we considering using AI over other chips such as GPUs? There are three reasons: computing efficiency, power consumption and future proofing. FPGAs are computing efficient because of their high customisability, which means we have better degree of control over how we move and process data. This means we can achieve a very low consistent latency, which GPUs cannot do. On top of that modern FPGAs contain hundreds of hardware blocks that can perform large amounts of calculations simultaneously outclassing CPUs.
FPGAs are also power efficient. They provide comparatively good performance per Watt compared to GPU technology. This is important because many applications are power limited, but still require large amounts of computation, such as wearables, VR, or robotics. This is also important in data centres, where power capacity and cooling are impact factors.
Finally, FPGAs provide a degree of future proofing that other chips cannot match. AI is a constantly evolving field and often changes in software occur too quickly for hardware accelerators to catch up. FPGAs can provide a needed boost in performance whilst also adapting to the latest and greatest AI techniques. Also, the chip does not need to be devoted to AI alone, meaning if you need to perform other tasks alongside AI, it can accelerate those tasks as well. For these three reasons, we are likely to see FPGAs increase their usage in the AI field. To find out more about applying FPGAs to your AI workloads visit our website.