# An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs

arXiv: https://arxiv.org/abs/2001.01955 · HTML: https://mlsanity.com/paper/2001.01955/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 16 of 16)

- [CHIP: CHannel Independence-based Pruning for Compact Neural Networks](https://mlsanity.com/paper/2110.13981/) (2021)
- [TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs](https://mlsanity.com/paper/2311.12862/) (2023)
- [An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers](https://mlsanity.com/paper/2208.06118/) (2022)
- [Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks](https://mlsanity.com/paper/2112.13896/) (2021)
- [Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads](https://mlsanity.com/paper/2412.11702/) (2024)
- [CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness](https://mlsanity.com/paper/2212.01957/) (2022)
- [HYDRA: Hybrid Data Multiplexing and Run-time Layer Configurable DNN Accelerator](https://mlsanity.com/paper/2409.04976/) (2024)
- [EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators](https://mlsanity.com/paper/2202.02310/) (2022)
- [HASS: Hardware-Aware Sparsity Search for Dataflow DNN Accelerator](https://mlsanity.com/paper/2406.03088/) (2024)
- [Dynamically Reconfigurable Variable-precision Sparse-Dense Matrix Acceleration in Tensorflow Lite](https://mlsanity.com/paper/2304.08211/) (2023)
- [Cross-Spectral Attention for Unsupervised RGB-IR Face Verification and Person Re-identification](https://mlsanity.com/paper/2411.19215/) (2024)
- [Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval](https://mlsanity.com/paper/2504.11879/) (2025)
- [Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems](https://mlsanity.com/paper/2507.01429/) (2025)
- [SparsePixels: Efficient Convolution for Sparse Data on FPGAs](https://mlsanity.com/paper/2512.06208/) (2025)
- [Breaking the accuracy-resource dilemma: a lightweight adaptive video inference enhancement](https://mlsanity.com/paper/2601.14568/) (2026)
- [SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC](https://mlsanity.com/paper/2606.09946/) (2026)

## Abstract (arXiv)

Deep Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in a wide range of applications. However, deeper CNN models, which are usually computation consuming, are widely required for complex Artificial Intelligence (AI) tasks. Though recent research progress on network compression such as pruning has emerged as a promising direction to mitigate computational burden, existing accelerators are still prevented from completely utilizing the benefits of leveraging sparsity owing to the irregularity caused by pruning. On the other hand, Field-Programmable Gate Arrays (FPGAs) have been regarded as a promising hardware platform for CNN inference acceleration. However, most existing FPGA accelerators focus on dense CNN and cannot address the irregularity problem. In this paper, we propose a sparse wise dataflow to skip the cycles of processing Multiply-and-Accumulates (MACs) with zero weights and exploit data statistics to minimize energy through zeros gating to avoid unnecessary computations. The proposed sparse wise dataflow leads to a low bandwidth requirement and a high data sharing. Then we design an FPGA accelerator containing a Vector Generator Module (VGM) which can match the index between sparse weights and input activations according to the proposed dataflow. Experimental results demonstrate that our implementation can achieve 987 imag/s and 48 imag/s performance for AlexNet and VGG-16 on Xilinx ZCU102, respectively, which provides 1.5x to 6.7x speedup and 2.0x to 6.2x energy-efficiency over previous CNN FPGA accelerators.