# Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning

arXiv: https://arxiv.org/abs/1803.05909 · HTML: https://mlsanity.com/paper/1803.05909/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge](https://mlsanity.com/paper/2110.14032/) (2021)
- [Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training](https://mlsanity.com/paper/2209.11204/) (2022)
- [GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity](https://mlsanity.com/paper/2108.11033/) (2021)
- [Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks](https://mlsanity.com/paper/2211.12714/) (2022)

## Abstract (arXiv)

The recent trend toward increasingly deep convolutional neural networks (CNNs) leads to a higher demand of computational power and memory storage. Consequently, the deployment of CNNs in hardware has become more challenging. In this paper, we propose an Intra-Kernel Regular (IKR) pruning scheme to reduce the size and computational complexity of the CNNs by removing redundant weights at a fine-grained level. Unlike other pruning methods such as Fine-Grained pruning, IKR pruning maintains regular kernel structures that are exploitable in a hardware accelerator. Experimental results demonstrate up to 10x parameter reduction and 7x computational reduction at a cost of less than 1% degradation in accuracy versus the un-pruned case.