Building Intelligence into Machine Learning Hardware

Machine learning is a rising star in the compute constellation, and for good reason. It has the ability to not only make life more convenient – think email spam filtering, shopping recommendations, and the like – but also to save lives by powering the intelligence behind autonomous vehicles, heart attack prediction, etc. While the applications of machine learning are bounded only by imagination, the execution of those applications is bounded by the available compute resources. Machine learning is compute-intensive and it turns out that traditional compute hardware is not well-suited for the task. Many machine learning shops have approached the problem with graphics processing units (GPUs), application-specific integrated circuits (ASICs) – for example, Google TensorFlow – or field-programmable gate arrays (FPGAs) – for example, Microsoft's investment in FPGAs for Azure and Amazon's announcement of FPGA instances.