The success of algorithms can be traced back to the hardware that mounts them. The explosion of System-on-Chip customised hardware onto the AI scene has revolutionised many real-world applications. Chips for AI acceleration have tremendous implications for applying AI to domains under significant constraints such as size, weight and power, both in embedded applications and in data centres.

In a survey supported by the Assistant Secretary of Defense for Research and Engineering under the Air Force, the researchers from MIT Lincoln Laboratory Supercomputing Center discussed the current state of machine learning hardware and what the future holds. Over the past few months, we have seen many releases from top chip makers like Nvidia and Intel. There have been other announcements too which were kept under wraps for later this year or next year. In this article, we take a look at accelerator chips, that according to the survey, have been announced but have not published any performance and power numbers.

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