38 citations · 42 across the 8 of their papers we have counts for
12 papers · 1 filter
A Framework for Accelerating Transformer Inference on RISC-V for Edge AI
Ajay Kumar M, Vishnu PS, Yike Li +5
This work presents a framework for accelerating transformer-based language models (LMs) on resource-constrained IoT devices. The framework targets compact LMs: BERT-Tiny (B-Ty), Mo…
A Pipelined FPGA Architecture for Banded Sparse Matrix Dense Matrix Multiplication in Longformer
Phillip Pramberger, Athanasios Tziouvaras, Shreejith Shanker +1
Sparse attention mechanisms have become increasingly important for transformer models processing long input sequences due to their lower computational and memory complexity compare…
Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks
Oran Hayes, Maria Pantazi-Kypraiou, Athanasios Tziouvaras +4
Power Delivery Networks (PDNs) are critical components of modern VLSI chips, providing stable voltage levels while satisfying electromigration (EM) and IR-drop constraints. Convent…
FINNAS: FINN-Guided Hardware-Aware NAS and Pruning for FPGA Jet Substructure Classification
Eva Chauffour, Changhong Li, Georgios Floros +1
FPGAs are well suited to deploying quantised neural networks (QNNs) under strict accuracy, latency, and resource constraints; however, identifying efficient model-accelerator combi…
FSNIC: A Low-Latency Flow-Based Intrusion Detection Architecture for FPGA SmartNICs
Nise O'Cuill, Changhong Li, Georgios Floros +1
Modern data centres require high-performance networking alongside effective real-time security. Traditional Intrusion Detection Systems (IDS) commonly rely on general-purpose proce…
EBL: Efficient Broad Learning for Distributed Adaptive Harmonic Analysis
Changhong Li, Georgios Floros, Biswajit Basu +1
Renewable energy systems and electrified transport have found widespread adoption in recent years. The integration of these non-linear loads, dominated by electric vehicle (EV) cha…
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…
Workload-Aware Early-Stage Power Delivery Network Optimization via Architectural Power Traces
Oran Hayes, Maria Pantazi-Kypraiou, Athanasios Tziouvaras +4
Power Delivery Networks (PDNs) are critical for maintaining voltage integrity in modern multiprocessor systems. Conventional early-stage PDN planning relies on static or worst-case…
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…
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…