38 citations · 42 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs
Changhong Li, Clément Bled, Rosa Fernandez +1
Denoising is a core operation in modern video pipelines. In codecs, in-loop filters suppress sensor noise and quantisation artefacts to improve rate-distortion performance; in cine…
MARVEL: An End-to-End Framework for Generating Model-Class Aware Custom RISC-V Extensions for Lightweight AI
Ajay Kumar M, Cian O'Mahoney, Pedro Kreutz Werle +5
Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Ex…
Bare-Metal RISC-V + NVDLA SoC for Efficient Deep Learning Inference
Vineet Kumar, Ajay Kumar M, Yike Li +2
This paper presents a novel System-on-Chip (SoC) architecture for accelerating complex deep learning models for edge computing applications through a combination of hardware and so…
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…