77 citations · 163 across the 20 of their papers we have counts for
21 papers
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
Hongxiang Fan, Thomas Chau, Stylianos I. Venieris +5
Attention-based neural networks have become pervasive in many AI tasks. Despite their excellent algorithmic performance, the use of the attention mechanism and feed-forward network…
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
Zhiqiang Que, Hongxiang Fan, Marcus Loo +5
This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performanc…
FLiMS: a Fast Lightweight 2-way Merger for Sorting
Philippos Papaphilippou, Wayne Luk, Chris Brooks
In this paper, we present FLiMS, a highly-efficient and simple parallel algorithm for merging two sorted lists residing in banked and/or wide memory. On FPGAs, its implementation u…
Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator
Hongxiang Fan, Martin Ferianc, Zhiqiang Que +4
Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware desig…
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
Zhiqiang Que, Erwei Wang, Umar Marikar +10
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational i…
Extending the RISC-V ISA for exploring advanced reconfigurable SIMD instructions
Philippos Papaphilippou, Paul H. J. Kelly, Wayne Luk
This paper presents a novel, non-standard set of vector instruction types for exploring custom SIMD instructions in a softcore. The new types allow simultaneous access to a relativ…
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
Martin Ferianc, Zhiqiang Que, Hongxiang Fan +2
Neural networks have demonstrated their outstanding performance in a wide range of tasks. Specifically recurrent architectures based on long-short term memory (LSTM) cells have man…
High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Hongxiang Fan, Martin Ferianc, Miguel Rodrigues +3
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs…
An Analysis of Alternating Direction Method of Multipliers for Feed-forward Neural Networks
Seyedeh Niusha Alavi Foumani, Ce Guo, Wayne Luk
In this work, we present a hardware compatible neural network training algorithm in which we used alternating direction method of multipliers (ADMM) and iterative least-square meth…
An FPGA Accelerated Method for Training Feed-forward Neural Networks Using Alternating Direction Method of Multipliers and LSMR
Seyedeh Niusha Alavi Foumani, Ce Guo, Wayne Luk
In this project, we have successfully designed, implemented, deployed and tested a novel FPGA accelerated algorithm for neural network training. The algorithm itself was developed…
Improving Performance Estimation for FPGA-based Accelerators for Convolutional Neural Networks
Martin Ferianc, Hongxiang Fan, Ringo S. W. Chu +2
Field-programmable gate array (FPGA) based accelerators are being widely used for acceleration of convolutional neural networks (CNNs) due to their potential in improving the perfo…
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification
Ringo S. W. Chu, Ho-Cheung Ng, Xiwei Wang +1
Hyperspectral images (HSIs) can distinguish materials with high number of spectral bands, which is widely adopted in remote sensing applications and benefits in high accuracy land…
Optimizing CNN-based Hyperspectral Image Classification on FPGAs
Shuanglong Liu, Ringo S. W. Chu, Xiwei Wang +1
Hyperspectral image (HSI) classification has been widely adopted in applications involving remote sensing imagery analysis which require high classification accuracy and real-time…
Pangloss: a novel Markov chain prefetcher
Philippos Papaphilippou, Paul H. J. Kelly, Wayne Luk
We present Pangloss, an efficient high-performance data prefetcher that approximates a Markov chain on delta transitions. With a limited information scope and space/logic complexit…
Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going
Erwei Wang, James J. Davis, Ruizhe Zhao +5
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, howe…
Efficient Structured Pruning and Architecture Searching for Group Convolution
Ruizhe Zhao, Wayne Luk
Efficient inference of Convolutional Neural Networks is a thriving topic recently. It is desirable to achieve the maximal test accuracy under given inference budget constraints whe…
Towards Efficient Convolutional Neural Network for Domain-Specific Applications on FPGA
Ruizhe Zhao, Ho-Cheung Ng, Wayne Luk +1
FPGA becomes a popular technology for implementing Convolutional Neural Network (CNN) in recent years. Most CNN applications on FPGA are domain-specific, e.g., detecting objects fr…
Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service
Gordon Inggs, David B. Thomas, George Constantinides +1
In the near future FPGAs will be available by the hour, however this new Infrastructure as a Service (IaaS) usage mode presents both an opportunity and a challenge: The opportunity…
A Domain Specific Approach to High Performance Heterogeneous Computing
Gordon Inggs, David B. Thomas, Wayne Luk
Users of heterogeneous computing systems face two problems: firstly, in understanding the trade-off relationships between the observable characteristics of their applications, such…
A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility
Gordon Inggs, David Thomas, Wayne Luk
We advocate a domain specific software development methodology for heterogeneous computing platforms such as Multicore CPUs, GPUs and FPGAs. We argue that three specific benefits a…
Evaluation of SystemC Modelling of Reconfigurable Embedded Systems
Tero Rissa, Adam Donlin, Wayne Luk
This paper evaluates the use of pin and cycle accurate SystemC models for embedded system design exploration and early software development. The target system is MicroBlaze Vanilla…