10 citations · 40 across the 13 of their papers we have counts for
13 papers
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Xi Xie, Hongwu Peng, Amit Hasan +7
Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by…
Neuromorphic Online Learning for Spatiotemporal Patterns with a Forward-only Timeline
Zhenhang Zhang, Jingang Jin, Haowen Fang +1
Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns…
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Shaoyi Huang, Haowen Fang, Kaleel Mahmood +7
Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event…
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
Nuo Xu, Kaleel Mahmood, Haowen Fang +3
Spiking neural networks (SNNs) have attracted much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep le…
In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor
Amar Shrestha, Haowen Fang, Daniel Patrick Rider +2
Although widely used in machine learning, backpropagation cannot directly be applied to SNN training and is not feasible on a neuromorphic processor that emulates biological neuron…
Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning
Haowen Fang, Brady Taylor, Ziru Li +3
Neuromorphic computing and spiking neural networks (SNN) mimic the behavior of biological systems and have drawn interest for their potential to perform cognitive tasks with high e…
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
Bingbing Li, Santosh Pandey, Haowen Fang +7
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using seque…
Multivariate Time Series Classification Using Spiking Neural Networks
Haowen Fang, Amar Shrestha, Qinru Qiu
There is an increasing demand to process streams of temporal data in energy-limited scenarios such as embedded devices, driven by the advancement and expansion of Internet of Thing…
MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level
Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang +1
Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spa…
GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction
Ziyi Zhao, Haowen Fang, Zhao Jin +1
The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are…
Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network
Haowen Fang, Amar Shrestha, Ziyi Zhao +1
The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However…
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET
Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang +1
When the navigational environment is known, it can be represented as a graph where landmarks are nodes, the robot behaviors that move from node to node are edges, and the route is…
Scalable NoC-based Neuromorphic Hardware Learning and Inference
Haowem Fang, Amar Shrestha, De Ma +1
Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparse…