1. Bridging Python to Silicon: The SODA Toolchain.
- Author
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Agostini, Nicolas Bohm, Curzel, Serena, Zhang, Jeff Jun, Limaye, Ankur, Tan, Cheng, Amatya, Vinay, Minutoli, Marco, Castellana, Vito Giovanni, Manzano, Joseph, Brooks, David, Wei, Gu-Yeon, and Tumeo, Antonino
- Subjects
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COMPILERS (Computer programs) , *APPLICATION-specific integrated circuits , *CONVOLUTIONAL neural networks , *FIELD programmable gate arrays , *SOFTWARE frameworks , *PYTHON programming language - Abstract
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 software-defined accelerator (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 multilevel representations to progressively lower and optimize the input code. Our approach does not require the application developer to write any register-transfer level code, and it is able to reach up to 364 giga floating point operations per second (GFLOPS)/W efficiency (32-bit precision) on typical convolutional neural network operators. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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