计算机科学
敏捷软件开发
软件工程
软件
程序设计语言
模块化设计
过程(计算)
作者
Jeff Jun Zhang,Nicolas Bohm Agostini,Shihao Song,Cheng Tan,Ankur Limaye,Vinay Amatya,Joseph Manzano,Marco Minutoli,Vito Giovanni Castellana,Antonino Tumeo,Gu-Yeon Wei,David Brooks
出处
期刊:Application-Specific Systems, Architectures, and Processors
日期:2021-07-07
卷期号:: 218-225
标识
DOI:10.1109/asap52443.2021.00040
摘要
Domain-specific designs offer greater energy efficiency and performance gain than general-purpose processors. For this reason, modern system-on-chips have a significant portion of their silicon area with custom accelerators. However, designing hardware by hand is laborious and time-consuming, given the large design space and the performance, power, and area constraints that are not realized in the software. Moreover, domain-specific algorithms (e.g., machine learning models) are evolving quickly, challenging the accelerator design further. To address these issues, this paper presents SODA Synthesizer, an automated open-source high-level ML framework to Verilog modular compiler targeting AI/ML Application-Specific Integrated Circuits (ASICs) accelerators. SODA tightly couples the Multi-Level Intermediate Representation (MLIR) compiler infrastructure [24] and open-source HLS approaches. Thus, SODA can support various ML frameworks and algorithms and can perform optimizations that combine specialized architecture templates and conventional HLS to generate the hardware modules. In addition, SODA’s closed-loop design space exploration (DSE) engine allows developers to perform end-to-end design space explorations on different metrics and technology nodes.
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