Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurable DNN inference accelerators which allows for the exploration of accelerator designs and configuration space. However, preparing models for evaluation and exploring configuration space in STONNE is a manual developer-timeconsuming process, which is a barrier for research. This paper introduces Bifrost, an end-to-end framework for the evaluation and optimization of reconfigurable DNN inference accelerators. Bifrost operates as a frontend for STONNE and leverages the TVM deep learning compiler stack to parse models and automate offloading of accelerated computations. We discuss Bifrost's advantages over STONNE and other tools, and evaluate the MAERI and SIGMA architectures using Bifrost. Additionally, Bifrost introduces a module leveraging AutoTVM to efficiently explore accelerator designs and dataflow mapping space to optimize performance. This is demonstrated by tuning the MAERI architecture and generating efficient dataflow mappings for AlexNet, obtaining an average speedup of 50× for the convolutional layers and 11× for the fully connected layers. Our code is available at www.github.com/gicLAB/bifrost.
@article{arxiv.2204.12418,
title = {Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators},
author = {Axel Stjerngren and Perry Gibson and José Cano},
journal= {arXiv preprint arXiv:2204.12418},
year = {2022}
}