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arXiv · 2511.03079

LogicSparse: Enabling Engine-Free Unstructured Sparsity for Quantised Deep-learning Accelerators

Changhong Li·Biswajit Basu·Shreejith Shanker
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Abstract

FPGAs have been shown to be a promising platform for deploying Quantised Neural Networks (QNNs) with high-speed, low-latency, and energy-efficient inference. However, the complexity of modern deep-learning models limits the performance on resource-constrained edge devices. While quantisation and pruning alleviate these challenges, unstructured sparsity remains underexploited due to irregular memory access. This work introduces a framework that embeds unstructured sparsity into dataflow accelerators, eliminating the need for dedicated sparse engines and preserving parallelism. A hardware-aware pruning strategy is introduced to improve efficiency and design flow further. On LeNet-5, the framework attains 51.6 x compression and 1.23 x throughput improvement using only 5.12% of LUTs, effectively exploiting unstructured sparsity for QNN acceleration.

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Changhong Li, Biswajit Basu, Shreejith Shanker. 2025-11-05. LogicSparse: Enabling Engine-Free Unstructured Sparsity for Quantised Deep-learning Accelerators. https://arxiv.org/abs/2511.03079

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