An Open-Source ML-Based Full-Stack Optimization Framework for Machine
  Learning Accelerators
Hadi Esmaeilzadeh Soroush Ghodrati Andrew B. Kahng Joon Kyung Kim Sean Kinzer Sayak Kundu Rohan Mahapatra Susmita Dey Manasi Sachin Sapatnekar Zhiang Wang Ziqing Zeng
Parameterizable machine learning (ML) accelerators are the product of recent
breakthroughs in ML. To fully enable their design space exploration (DSE), we
propose a physical-design-driven, learning-based prediction framework for
hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It
adopts a unified approach that combines backend power, performance, and area
(PPA) analysis with frontend performance simulation, thereby achieving a
realistic estimation of both backend PPA and system metrics such as runtime and
energy. In addition, our framework includes a fully automated DSE technique,
which optimizes backend and system metrics through an automated search of
architectural and backend parameters. Experimental studies show that our
approach consistently predicts backend PPA and system metrics with an average
7% or less prediction error for the ASIC implementation of two deep learning
accelerator platforms, VTA and VeriGOOD-ML, in both a commercial 12 nm process
and a research-oriented 45 nm process.