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ESL-former: Event Sparse Linear Transformer for FPGA-Accelerated Malware Detection
Jun Yang, Yumeng Cao, Yingxuan Xiao +2
Transformer based intrusion detection has shown strong potential for identifying unknown malware variants, but its deployment on resource constrained edge devices remains difficult…
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…
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…
FAV-NSS: An HIL Framework for Accelerating Validation of Automotive Network Security Strategies
Changhong Li, Shashwat Khandelwal, Shreejith Shanker
Complex electronic control unit (ECU) architectures, software models and in-vehicle networks are consistently improving safety and comfort functions in modern vehicles. However, th…