Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms

Abstract

Parameterizable ML accelerators are the product of recent breakthroughs in machine learning (ML). To fully enable the design space exploration, we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It employs a unified methodology, coupling backend power, performance and area (PPA) analysis with frontend performance simulation, thus achieving realistic estimation of both backend PPA and system metrics (runtime and energy). Experimental studies show that the approach provides excellent predictions for both ASIC (in a 12nm commercial process) and FPGA implementations on the VTA and VeriGOOD-ML platforms.