Bridging Python to Silicon: The SODA Toolchain

Systems performing scientific computing, data analysis, and machine learning tasks have a growing demand for application-specific accelerators that can provide high computational performance while meeting strict size and power requirements. However, the algorithms and applications that need to be accelerated are evolving at a rate that is incompatible with manual design processes based on hardware description languages. Agile hardware design tools based on compiler techniques can help by quickly producing an application specific integrated circuit (ASIC) accelerator starting from a high-level algorithmic description. We present the SODA Synthesizer, a modular and open-source hardware compiler that provides automated end-to-end synthesis from high-level software frameworks to ASIC implementation, relying on multi-level representations to progressively lower and optimize the input code. Our approach does not require the application developer to write register-transfer level code, and it is able to reach up to 364 GFLOPS/W efficiency (32-bit precision) on typical convolutional neural network operators