@inproceedings{708d0f197a474eabad2710a42a69626a,
title = "ThUnderVolt: Enabling aggressive voltage underscaling and timing error resilience for energy efficient deep learning accelerators",
abstract = "Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference. In this paper we propose Thundervolt, a new framework that enables aggressive voltage underscaling of high-performance DNN accelerators without compromising classification accuracy even in the presence of high timing error rates. Using post-synthesis timing simulations of a DNN accelerator modeled on the Google TPU, we show that Thundervolt enables between 34%-57% energy savings on state-of-the-art speech and image recognition benchmarks with less than 1% loss in classification accuracy and no performance loss. Further, we show that Thundervolt is synergistic with and can further increase the energy efficiency of commonly used run-time DNN pruning techniques like Zero-Skip.",
author = "Jeff Zhang and Kartheek Rangineni and Zahra Ghodsi and Siddharth Garg",
year = "2018",
month = jun,
day = "24",
doi = "10.1145/3195970.3196129",
language = "English (US)",
isbn = "9781450357005",
series = "Proceedings - Design Automation Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 55th Annual Design Automation Conference, DAC 2018",
note = "55th Annual Design Automation Conference, DAC 2018 ; Conference date: 24-06-2018 Through 29-06-2018",
}