DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators

Sheng-Chun Kao1,a, Michael Pellauer2,c, Angshuman Parashar2,d and Tushar Krishna1,b
1Georgia Institute of Technology
askao6@gatech.edu
btushar@ece.gatech.edu
2NVIDIA
cmpellauer@nvidia.com
daparashar@nvidia.com

ABSTRACT


The design of DNN accelerators includes two key parts: HW resource configuration and mapping strategy. Intensive research has been conducted to optimize each of them independently. Unfortunately, optimizing for both together is extremely challenging due to the extremely large cross-coupled search space. To address this, in this paper, we propose a HW-Mapping cooptimization framework, an efficient encoding of the immense design space constructed by HW and Mapping, and a domainaware genetic algorithm, named DiGamma, with specialized operators for improving search efficiency. We evaluate DiGamma with seven popular DNNs models with different properties. Our evaluations show DiGamma can achieve (geomean) 3.0x and 10.0x speedup, comparing to the best-performing baseline optimization algorithms, in edge and cloud settings.



Full Text (PDF)