# Accelerator-Aware Pruning for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1804.09862 · HTML: https://mlsanity.com/paper/1804.09862/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2018.

## Built on by (most cited first, 22 of 22)

- [Accelerating Sparse Deep Neural Networks](https://mlsanity.com/paper/2104.08378/) (2021)
- [S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration](https://mlsanity.com/paper/2107.07983/) (2021)
- [Effective Interplay between Sparsity and Quantization: From Theory to Practice](https://mlsanity.com/paper/2405.20935/) (2024)
- [Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers](https://mlsanity.com/paper/2402.04744/) (2024)
- [Sparse maximal update parameterization: A holistic approach to sparse training dynamics](https://mlsanity.com/paper/2405.15743/) (2024)
- [Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning](https://mlsanity.com/paper/2209.14624/) (2022)
- [Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask](https://mlsanity.com/paper/2209.07617/) (2022)
- [Dual Discriminator Adversarial Distillation for Data-free Model Compression](https://mlsanity.com/paper/2104.05382/) (2021)
- [FPGA Resource-aware Structured Pruning for Real-Time Neural Networks](https://mlsanity.com/paper/2308.05170/) (2023)
- [Cut Less, Fold More: Model Compression through the Lens of Projection Geometry](https://mlsanity.com/paper/2602.18116/) (2026)
- [Resource Efficient Neural Networks Using Hessian Based Pruning](https://mlsanity.com/paper/2306.07030/) (2023)
- [KAN-SAs: Efficient Acceleration of Kolmogorov-Arnold Networks on Systolic Arrays](https://mlsanity.com/paper/2512.00055/) (2025)
- [Binarizing by Classification: Is soft function really necessary?](https://mlsanity.com/paper/2205.07433/) (2022)
- [From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks](https://mlsanity.com/paper/2405.06038/) (2024)
- [Compressing Deep Neural Networks Using Explainable AI](https://mlsanity.com/paper/2507.05286/) (2025)
- [Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems](https://mlsanity.com/paper/2411.10285/) (2024)
- [HASI: Hardware-Accelerated Stochastic Inference, A Defense Against Adversarial Machine Learning Attacks](https://mlsanity.com/paper/2106.05825/) (2021)
- [DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning](https://mlsanity.com/paper/2208.00498/) (2022)
- [Accelerator-Aware Training for Transducer-Based Speech Recognition](https://mlsanity.com/paper/2305.07778/) (2023)
- [PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator](https://mlsanity.com/paper/2408.16233/) (2024)
- [Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems](https://mlsanity.com/paper/2507.01429/) (2025)
- [Resource-Efficient Pruning for Transformer via Low-Rank Importance Estimation](https://mlsanity.com/paper/2608.24973/) (2026)

## Abstract (arXiv)

Convolutional neural networks have shown tremendous performance capabilities in computer vision tasks, but their excessive amounts of weight storage and arithmetic operations prevent them from being adopted in embedded environments. One of the solutions involves pruning, where certain unimportant weights are forced to have a value of zero. Many pruning schemes have been proposed, but these have mainly focused on the number of pruned weights. Previous pruning schemes scarcely considered ASIC or FPGA accelerator architectures. When these pruned networks are run on accelerators, the lack of consideration of the architecture causes some inefficiency problems, including internal buffer misalignments and load imbalances. This paper proposes a new pruning scheme that reflects accelerator architectures. In the proposed scheme, pruning is performed so that the same number of weights remain for each weight group corresponding to activations fetched simultaneously. In this way, the pruning scheme resolves the inefficiency problems, doubling the accelerator performance. Even with this constraint, the proposed pruning scheme reached a pruning ratio similar to that of previous unconstrained pruning schemes, not only on AlexNet and VGG16 but also on state-of-the-art very deep networks such as ResNet. Furthermore, the proposed scheme demonstrated a comparable pruning ratio on compact networks such as MobileNet and on slimmed networks that were already pruned in a channel-wise manner. In addition to improving the efficiency of previous sparse accelerators, it will be also shown that the proposed pruning scheme can be used to reduce the logic complexity of sparse accelerators.The pruned models are publicly available at https://github.com/HyeongjuKang/accelerator-aware-pruning.