Google Investment in Open Source Custom Hardware Development Including No-Cost Shuttle Program

The end of Moore's Law combined with unabated growth in usage have forced Google to turn to hardware acceleration to deliver efficiency gains to meet demand. Traditional hardware design methodology for accelerators is practical when there's a common core - such as with Machine Learning (ML) or video transcoding, but what about the hundreds of smaller tasks performed in Google data centers? Our vision is "software-speed" development for hardware acceleration so that it becomes commonplace and, frankly, boring. Toward this goal Google is investing in open tooling to foster innovation in multiplying accelerator developer productivity. Tim Ansell will provide an outline of these coordinated open source projects in EDA (including high level synthesis), IP, PDKs, and related areas. This will be followed by presenting the CFU (Custom Function Unit) Playground, which utilizes many of these projects. The CFU Playground lets you build your own specialized & optimized ML processor based on the open RISC-V ISA, implemented on an FPGA using a fully open source stack. The goal isn't general ML extensions; it's about a methodology for building your own extension specialized just for your specific tiny ML model. The extension can range from a few simple new instructions, up to a complex accelerator that interfaces to the CPU via a set of custom instructions; we will show examples of both.