177 citations · 340 across the 24 of their papers we have counts for
24 papers
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…
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…
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…
Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction
Ziyi Zhao, Zhao Jin, Wentian Bai +4
The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an eme…
Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations
Zhao Jin, Ziyi Zhao, Chen Luo +5
Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related…
Embedding Compression with Isotropic Iterative Quantization
Siyu Liao, Jie Chen, Yanzhi Wang +2
Continuous representation of words is a standard component in deep learning-based NLP models. However, representing a large vocabulary requires significant memory, which can cause…
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…
CircConv: A Structured Convolution with Low Complexity
Siyu Liao, Zhe Li, Liang Zhao +3
Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications. However, the lar…
A Simulation Framework for Fast Design Space Exploration of Unmanned Air System Traffic Management Policies
Ziyi Zhao, Chen Luo, Jin Zhao +4
The number of daily small Unmanned Aircraft Systems (sUAS) operations in uncontrolled low altitude airspace is expected to reach into the millions. UAS Traffic Management (UTM) is…
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Zhe Li, Caiwen Ding, Siyue Wang +8
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types…
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…
Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing
Zhe Li, Ji Li, Ao Ren +5
Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications. Nevertheless, the deep structure has brought significant i…
Learning Topics using Semantic Locality
Ziyi Zhao, Krittaphat Pugdeethosapol, Sheng Lin +4
The topic modeling discovers the latent topic probability of the given text documents. To generate the more meaningful topic that better represents the given document, we proposed…
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Zhe Li, Shuo Wang, Caiwen Ding +3
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning…
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
Shuo Wang, Zhe Li, Caiwen Ding +4
Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately,…
C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing
Zhe Li, Xiaolong Ma, Hongjia Li +5
Increasing malicious users have sought practices to leverage 3D printing technology to produce unlawful tools in criminal activities. Current regulations are inadequate to deal wit…
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Yanzhi Wang, Caiwen Ding, Zhe Li +8
Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and pe…
CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices
Caiwen Ding, Siyu Liao, Yanzhi Wang +13
Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performanc…
Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks
Ji Li, Zihao Yuan, Zhe Li +5
Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks.…
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Ning Liu, Zhe Li, Zhiyuan Xu +5
Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing…
SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing
Ao Ren, Ji Li, Zhe Li +5
With recent advancing of Internet of Things (IoTs), it becomes very attractive to implement the deep convolutional neural networks (DCNNs) onto embedded/portable systems. Presently…