Search Results for author: Qinru Qiu

Found 21 papers, 1 papers with code

In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor

no code implementations8 May 2021 Amar Shrestha, Haowen Fang, Daniel Patrick Rider, Zaidao Mei, Qinru Qiu

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 and synapses.

BIG-bench Machine Learning

Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning

no code implementations21 Apr 2021 Haowen Fang, Brady Taylor, Ziru Li, Zaidao Mei, Hai Li, Qinru Qiu

This circuit implementation of the neuron model is simulated to demonstrate its ability to react to temporal spiking patterns with an adaptive threshold.

Association

MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level

no code implementations6 Jun 2020 Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang, Qinru Qiu

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.

Image Captioning Natural Language Queries +2

GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction

no code implementations22 Mar 2020 Ziyi Zhao, Haowen Fang, Zhao Jin, Qinru Qiu

The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system.

Autonomous Driving Trajectory Prediction

Embedding Compression with Isotropic Iterative Quantization

no code implementations11 Jan 2020 Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, Bo Yuan

Continuous representation of words is a standard component in deep learning-based NLP models.

Image Retrieval Quantization +1

High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET

no code implementations8 Jan 2020 Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang, Qinru Qiu

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 a set of behavioral instructions.

Combinatorial Optimization Robot Navigation

CircConv: A Structured Convolution with Low Complexity

no code implementations28 Feb 2019 Siyu Liao, Zhe Li, Liang Zhao, Qinru Qiu, Yanzhi Wang, Bo Yuan

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have emerged as the powerful technique in various machine learning applications.

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

no code implementations12 Dec 2018 Zhe Li, Caiwen Ding, Siyue Wang, Wujie Wen, Youwei Zhuo, Chang Liu, Qinru Qiu, Wenyao Xu, Xue Lin, Xuehai Qian, Yanzhi Wang

It is a challenging task to have real-time, efficient, and accurate hardware RNN implementations because of the high sensitivity to imprecision accumulation and the requirement of special activation function implementations.

Automatic Speech Recognition Quantization +2

Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing

no code implementations10 May 2018 Zhe Li, Ji Li, Ao Ren, Caiwen Ding, Jeffrey Draper, Qinru Qiu, Bo Yuan, Yanzhi Wang

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications.

Learning Topics using Semantic Locality

no code implementations11 Apr 2018 Ziyi Zhao, Krittaphat Pugdeethosapol, Sheng Lin, Zhe Li, Caiwen Ding, Yanzhi Wang, Qinru Qiu

The topic modeling discovers the latent topic probability of the given text documents.

Topic Models

C3PO: Database and Benchmark for Early-stage Malicious Activity Detection in 3D Printing

no code implementations20 Mar 2018 Zhe Li, Xiaolong Ma, Hongjia Li, Qiyuan An, Aditya Singh Rathore, Qinru Qiu, Wenyao Xu, Yanzhi Wang

It is of vital importance to enable 3D printers to identify the objects to be printed, so that the manufacturing procedure of an illegal weapon can be terminated at the early stage.

Action Detection Activity Detection +1

Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

no code implementations20 Mar 2018 Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Yun Liang

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.

Model Compression Time Series

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

no code implementations14 Mar 2018 Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Yanzhi Wang, Qinru Qiu, Yun Liang

The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

no code implementations13 Mar 2017 Ning Liu, Zhe Li, Zhiyuan Xu, Jielong Xu, Sheng Lin, Qinru Qiu, Jian Tang, Yanzhi Wang

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system.

Decision Making Management +2

Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

no code implementations12 Mar 2017 Ji Li, Zihao Yuan, Zhe Li, Caiwen Ding, Ao Ren, Qinru Qiu, Jeffrey Draper, Yanzhi Wang

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.

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

no code implementations18 Nov 2016 Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

Stochastic Computing (SC), which uses bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has a high potential for implementing DCNNs with high scalability and ultra-low hardware footprint.

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