Even Aldous Huxley had trouble foreseeing a Brave New World where driverless vehicles moved through major metropolitan areas and machines were able to learn. His ‘alphas’ might have had personal helicopters but they were expected to pilot them themselves.
Mr. Huxley, welcome to the era of autonomous driving, medical diagnostics, smart appliances, industrial automation, adaptive websites, financial analytics and so much more – all propelled by machine learning and artificial intelligence.
These applications demand high performance, and in most cases, low latency to respond to changing real-time conditions. For inference functions, the applications will be in end-user products that require low-power consumption. They will not have the plentiful power and cooling that is available for learning functions that reside in cloud-based servers. These applications will also typically have an always-on requirement whether there is a network connection to the cloud or not.
Hardware design must therefore now evolve to meet these and many more opportunities for commercial and industrial systems that are on the horizon. That evolution is likely to involve embedded FPGAs (eFPGAs) in SoCs.