# ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators

arXiv: https://arxiv.org/abs/1802.03806 · HTML: https://mlsanity.com/paper/1802.03806/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2018.

## Built on by (most cited first, 18 of 18)

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- [Shavette: Low Power Neural Network Acceleration via Algorithm-level Error Detection and Undervolting](https://mlsanity.com/paper/2410.13415/) (2024)
- [Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors](https://mlsanity.com/paper/2507.18804/) (2025)
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## Abstract (arXiv)

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.