24 citations · 58 across the 15 of their papers we have counts for
15 papers
DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs
Myat Thu Linn Aung, Daniel Gerlinghoff, Chuping Qu +5
Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving gr…
LightRW: FPGA Accelerated Graph Dynamic Random Walks
Hongshi Tan, Xinyu Chen, Yao Chen +2
Graph dynamic random walks (GDRWs) have recently emerged as a powerful paradigm for graph analytics and learning applications, including graph embedding and graph neural networks.…
Efficient Hyperdimensional Computing
Zhanglu Yan, Shida Wang, Kaiwen Tang +1
Hyperdimensional computing (HDC) is a method to perform classification that uses binary vectors with high dimensions and the majority rule. This approach has the potential to be en…
Desire Backpropagation: A Lightweight Training Algorithm for Multi-Layer Spiking Neural Networks based on Spike-Timing-Dependent Plasticity
Daniel Gerlinghoff, Tao Luo, Rick Siow Mong Goh +1
Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance. A major…
Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks
Zhanglu Yan, Jun Zhou, Weng-Fai Wong
Spiking neural networks (SNNs) are well suited for resource-constrained applications as they do not need expensive multipliers. In a typical rate-encoded SNN, a series of binary sp…
ReGraph: Scaling Graph Processing on HBM-enabled FPGAs with Heterogeneous Pipelines
Xinyu Chen, Yao Chen, Feng Cheng +3
The use of FPGAs for efficient graph processing has attracted significant interest. Recent memory subsystem upgrades including the introduction of HBM in FPGAs promise to further a…
Benchmarking Quantum(-inspired) Annealing Hardware on Practical Use Cases
Tian Huang, Jun Xu, Tao Luo +3
Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantu…
Optimizing for In-memory Deep Learning with Emerging Memory Technology
Zhehui Wang, Tao Luo, Rick Siow Mong Goh +2
In-memory deep learning computes neural network models where they are stored, thus avoiding long distance communication between memory and computation units, resulting in considera…
DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications
Tao Luo, Wai Teng Tang, Matthew Kay Fei Lee +3
Deep neural networks (DNN) have achieved remarkable success in computer vision (CV). However, training and inference of DNN models are both memory and computation intensive, incurr…
ThundeRiNG: Generating Multiple Independent Random Number Sequences on FPGAs
Hongshi Tan, Xinyu Chen, Yao Chen +2
In this paper, we propose ThundeRiNG, a resource-efficient and high-throughput system for generating multiple independent sequences of random numbers (MISRN) on FPGAs. Generating M…
Skew-Oblivious Data Routing for Data-Intensive Applications on FPGAs with HLS
Xinyu Chen, Hongshi Tan, Yao Chen +3
FPGAs have become emerging computing infrastructures for accelerating applications in datacenters. Meanwhile, high-level synthesis (HLS) tools have been proposed to ease the progra…
Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip
Bo Wang, Jun Zhou, Weng-Fai Wong +1
The next wave of on-device AI will likely require energy-efficient deep neural networks. Brain-inspired spiking neural networks (SNN) has been identified to be a promising candidat…
Extended social force model with a dynamic navigation field for bidirectional pedestrian flow
Yan-Qun Jiang, Bo-Kui Chen, Bing-Hong Wang +2
An extended social force model with a dynamic navigation field is proposed to study bidirectional pedestrian movement. The dynamic navigation field is introduced to describe the de…
PI : a Parallel in-memory skip list based Index
Zhongle Xie, Qingchao Cai, H. V. Jagadish +2
Due to the coarse granularity of data accesses and the heavy use of latches, indices in the B-tree family are not efficient for in-memory databases, especially in the context of to…
DGCC:A New Dependency Graph based Concurrency Control Protocol for Multicore Database Systems
Chang Yao, Divyakant Agrawal, Pengfei Chang +4
Multicore CPUs and large memories are increasingly becoming the norm in modern computer systems. However, current database management systems (DBMSs) are generally ineffective in e…