2 papers
cs.AR2026
RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs
Changhong Li, Biswajit Basu, Shreejith Shanker
High granularity quantisation (HGQ) exploits weight-level quantisation and pruning to design resource-efficient neural network accelerators, achieving an attractive trade-off betwe…
cs.AR2025
LogicSparse: Enabling Engine-Free Unstructured Sparsity for Quantised Deep-learning Accelerators
Changhong Li, Biswajit Basu, Shreejith Shanker
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 complexit…