# A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets

arXiv: https://arxiv.org/abs/1606.05094 · HTML: https://mlsanity.com/paper/1606.05094/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency](https://mlsanity.com/paper/2109.05223/) (2021)
- [Weight Fixing Networks](https://mlsanity.com/paper/2210.13554/) (2022)
- [SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks](https://mlsanity.com/paper/2510.19829/) (2025)
- [Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration](https://mlsanity.com/paper/2108.02569/) (2021)
- [Long-Term and Short-Term Transistor Aging in Deep Neural Networks: Impact and Mitigation](https://mlsanity.com/paper/2606.04266/) (2026)

## Abstract (arXiv)

A low-power precision-scalable processor for ConvNets or convolutional neural networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing units achieve a peak 102GOPS running at 204MHz. To minimize energy consumption while maintaining throughput, this works is the first to both exploit the sparsity of convolutions and to implement dynamic precision-scalability enabling supply- and energy scaling. The processor is fully C-programmable, consumes 25-288mW at 204 MHz and scales efficiency from 0.3-2.6 real TOPS/W. This system hereby outperforms the state-of-the-art up to 3.9x in energy efficiency.