VeriGOOD-ML: An Open-Source Flow for Automated ML Hardware Synthesis

Hadi Esmaeilzadeh, Soroush Ghodrati, Jie Gu, Shiyu Guo, Andrew B. Kahng, Joon Kyung Kim, Sean Kinzer, Rohan Mahapatra, Susmita Dey Manasi, Edwin Mascarenhas, Sachin S. Sapatnekar, Ravi Varadarajan, Zhiang Wang, Hanyang Xu, Brahmendra Reddy Yatham, Ziqing Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


This paper introduces VeriGOOD-ML, an automated methodology for generating Verilog with no human in the loop, starting from a high-level description of a machine learning (ML) algorithm in a standard format such as ONNX. The Verilog RTL is then translated through a back-end design flow to GDSII, driven by a design planning approach that is well tailored to the macro-intensive nature of ML platforms. VeriGOOD-ML uses three approaches to build ML hardware: the TABLA platform uses a dataflow architecture that is well suited to non-DNN ML algorithms; the GeneSys platform, with a systolic array and a SIMD array, is optimized for implementing DNNs; and the Axiline approach synthesizes small ML algorithms by hardcoding the structure of the algorithm into hardware, thus trading off flexibility for performance and power. The overall approach explores the design space of platform configurations and Pareto-optimal-PPA back-end implementations to yield designs that represent different tradeoffs at the algorithmic level between area, power, performance, and execution time. The overall methodology, from architecture to back-end design to hardware implementation, is described in this paper, and the results of VeriGOOD-ML are demonstrated on a set of ML benchmarks.

Original languageEnglish (US)
Title of host publication2021 40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665445078
StatePublished - 2021
Event40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021 - Munich, Germany
Duration: Nov 1 2021Nov 4 2021

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152


Conference40th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2021

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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