Brandon Reagen

TL;DR: Minerva as mentioned in this paper proposes a co-design approach across the algorithm, architecture, and circuit levels to optimize DNN hardware accelerators, and shows that fine-grained, heterogeneous dataatype optimization reduces power by 1.5× and aggressive, inline predication and pruning of small activity values further reduces power.

TL;DR: This paper takes a datadriven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.

TL;DR: Aladdin is presented, a pre-RTL, power-performance accelerator modeling framework and its application to system-on-chip (SoC) simulation and provides researchers an approach to model the power and performance of accelerators in an SoC environment.

TL;DR: This paper presents Ares: a light-weight, DNN-specific fault injection framework validated within 12% of real hardware, and finds that DNN fault tolerance varies by orders of magnitude with respect to model, layer type, and structure.

TL;DR: A set of real-world, production-scale DNNs for personalized recommendation coupled with relevant performance metrics for evaluation are presented and in-depth analysis is conducted that underpins future system design and optimization for at-scale recommendation.