# PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration

arXiv: https://arxiv.org/abs/2303.13997 · HTML: https://mlsanity.com/paper/2303.13997/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)

## Built on by (most cited first, 8 of 8)

- [Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression](https://mlsanity.com/paper/2410.03765/) (2024)
- [LiveMind: Low-latency Large Language Models with Simultaneous Inference](https://mlsanity.com/paper/2406.14319/) (2024)
- [Early-Exit with Class Exclusion for Efficient Inference of Neural Networks](https://mlsanity.com/paper/2309.13443/) (2023)
- [Layer-wise dynamic rank for compressing large language models](https://mlsanity.com/paper/2509.25622/) (2025)
- [Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks](https://mlsanity.com/paper/2306.07294/) (2023)
- [EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration](https://mlsanity.com/paper/2402.18595/) (2024)
- [Late Breaking Results: Conversion of Neural Networks into Logic Flows for Edge Computing](https://mlsanity.com/paper/2601.22151/) (2026)
- [DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity](https://mlsanity.com/paper/2609.09254/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the large number of multiply-and-accumulate (MAC) operations. To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accelerators by selecting weights that lead to less power consumption in MAC operations. In addition, the timing characteristics of the selected weights together with all activation transitions are evaluated. The weights and activations that lead to small delays are further selected. Consequently, the maximum delay of the sensitized circuit paths in the MAC units is reduced even without modifying MAC units, which thus allows a flexible scaling of supply voltage to reduce power consumption further. Together with retraining, the proposed method can reduce power consumption of DNNs on hardware by up to 78.3% with only a slight accuracy loss.