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

arXiv: https://arxiv.org/abs/2511.03079 · HTML: https://mlsanity.com/paper/2511.03079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [EBL: Efficient Broad Learning for Distributed Adaptive Harmonic Analysis](https://mlsanity.com/paper/2609.16358/) (2026)

## Abstract (arXiv)

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.