2019 IEEE International Workshop on Signal Processing Systems (SiPS)最新文献_第4页

DynExit: A Dynamic Early-Exit Strategy for Deep Residual Networks 深度残差网络的动态早退出策略

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020551

Meiqi Wang, Jianqiao Mo, Jun Lin, Zhongfeng Wang, L. Du

{"title":"DynExit: A Dynamic Early-Exit Strategy for Deep Residual Networks","authors":"Meiqi Wang, Jianqiao Mo, Jun Lin, Zhongfeng Wang, L. Du","doi":"10.1109/SiPS47522.2019.9020551","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020551","url":null,"abstract":"Early-exit is a kind of technique to terminate a pre-specified computation at an early stage depending on the input samples and has been introduced to reduce energy consumption for Deep Neural Networks (DNNs). Previous early-exit approaches suffered from the burden of manually tuning early-exit loss-weights to find a good trade-off between complexity reduction and system accuracy. In this work, we first propose DynExit, a dynamic loss-weight modification strategy for ResNets, which adaptively modifies the ratio of different exit branches and searches for a proper spot for both accuracy and cost. Then, an efficient hardware unit for early-exit branches is developed, which can be easily integrated to existing hardware architectures of DNNs to reduce average computing latency and energy cost. Experimental results show that the proposed DynExit strategy can reduce up to 43.6% FLOPS compared to the state-of-the-arts approaches. On the other hand, it is able to achieve 1.2% accuracy improvement over the existing end-to-end fixed loss-weight training scheme with comparable computation reduction ratio. The proposed hardware architecture for DynExit is evaluated on the platform of Xilinx Zynq-7000 ZC706 development board. Synthesis results demonstrate that the architecture can achieve high speed with low hardware complexity. To the best of our knowledge, this is the first hardware implementation for early-exit techniques used for DNNs in open literature.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132842777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Theoretical Analysis of Configurable RO PUFs and Strategies to Enhance Security 可配置RO puf的理论分析及提高安全性的策略

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020320

Jiang Li, Hao Gao, Yijun Cui, Chenghua Wang, Yale Wang, Chongyan Gu, Weiqiang Liu

引用次数: 2

Data Driven Low-Complexity DOA Estimation for Ultra-Short Range Automotive Radar 数据驱动的超近程汽车雷达低复杂度DOA估计

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020602

Yixin Song, Yang Li, Cheng Zhang, Yongming Huang

引用次数: 0

Efficiently Learning a Robust Self-Driving Model with Neuron Coverage Aware Adaptive Filter Reuse 有效学习具有神经元覆盖感知的自适应滤波器复用鲁棒自驾车模型

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020572

Chunpeng Wu, Ang Li, Bing Li, Yiran Chen

{"title":"Efficiently Learning a Robust Self-Driving Model with Neuron Coverage Aware Adaptive Filter Reuse","authors":"Chunpeng Wu, Ang Li, Bing Li, Yiran Chen","doi":"10.1109/SiPS47522.2019.9020572","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020572","url":null,"abstract":"Human drivers learn driving skills from both regular (non-accidental) and accidental driving experiences, while most of current self-driving research focuses on regular driving only. We argue that learning from accidental driving data is necessary for robustly modeling driving behavior. A main challenge, however, is how accident data can be effectively used together with regular data to learn vehicle motion, since manually labeling accident data without expertise is significantly difficult. Our proposed solution for robust vehicle motion learning, in this paper, is to integrate layer-level discriminability and neuron coverage(neuron-level robustness) regulariziers into an unsupervised generative network for video prediction. Layer-level discriminability increases divergence of feature distribution between the regular data and accident data at network layers. Neuron coverage regulariziers enlarge interval span of neuron activation adopted by training samples, to reduce probability that a sample falls into untested interval regions. To accelerate training process, we propose adaptive filter reuse based on neuron coverage. Our strategies of filter reuse reduce structural network parameters, encourage memory reuse, and guarantee effectiveness of robust vehicle motion learning. Experiments results show that our model improves the inference accuracy by 1.1% compared to FCMLSTM, and cut down 10.2% training time over the traditional method with negligible accuracy loss.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127840953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Data Structure-Based Approximate Belief Propagation Decoder for Polar Codes 一种基于数据结构的极码近似置信传播解码器

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020391

Menghui Xu, Weikang Qian, Zaichen Zhang, X. You, Chuan Zhang

引用次数: 0

Nonlinear Functions in Learned Iterative Shrinkage-Thresholding Algorithm for Sparse Signal Recovery 稀疏信号恢复学习迭代收缩阈值算法中的非线性函数

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020469

E. C. Marques, N. Maciel, L. Naviner, Hao Cai, Jun Yang

引用次数: 1

Side Channel Attack Resistant AES Design Based on Finite Field Construction Variation 基于有限域结构变化的抗侧信道攻击AES设计

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020535

P. Shvartsman, Xinmiao Zhang

引用次数: 3

PRESS/HOLD/RELEASE Ultrasonic Gestures and Low Complexity Recognition Based on TCN 基于TCN的按/持/放超声手势及低复杂度识别

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020579

Emad A. Ibrahim, Min Li, J. P. D. Gyvez

{"title":"PRESS/HOLD/RELEASE Ultrasonic Gestures and Low Complexity Recognition Based on TCN","authors":"Emad A. Ibrahim, Min Li, J. P. D. Gyvez","doi":"10.1109/SiPS47522.2019.9020579","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020579","url":null,"abstract":"Targeting ultrasound-based gesture recognition, this paper proposes a new universal PRESS/HOLD/RELEASE approach that leverages the diversity of gestures performed on smart devices such as mobile phones and IoT nodes. The new set of gestures are generated by interleaving PRESS/HOLD/RELEASE patterns; abbreviated as P/H/R, with gestures like sweeps between a number of microphones. P/H/R patterns are constructed by a hand as it approaches a top of a microphone to generate a virtual Press. After that, the hand settles for an undefined period of time to generate a virtual Hold and finally departs to generate a virtual Release. The same hand can sweep to a 2nd microphone and perform another P/H/R. Interleaving the P/H/R patterns expands the number of performed gestures. Assuming an on-board speaker transmitting ultrasonic signals, the detection is performed on Doppler shift readings generated by a hand as it approaches and departs a top of a microphone. The Doppler shift readings are presented in a sequence of down-mixed ultrasonic spectrogram frames. We train a Temporal Convolutional Network (TCN) to classify the P/H/R patterns under different environmental noises. Our experimental results show that such P/H/R patterns at a top of a microphone can be achieved with 96.6% accuracy under different noise conditions. A group of P/H/R based gestures has been tested on commercially off-the-shelf (COTS) Samsung Galaxy S7 Edge. Different P/H/R interleaved gestures (such as sweeps, long taps, etc.) are designed using two microphones and a single speaker while using as low as $sim 5mathrm{K}$ parameters and as low as $sim 0.15$ Million operations (MOPs) in compute power per inference. The P/H/R interleaved set of gestures are intuitive and hence are easy to learn by end users. This paves its way to be deployed by smartphones and smart speakers for mass production.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122717021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Neural Network-based Vehicle Image Classification for IoT Devices 基于神经网络的物联网设备车辆图像分类

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020464

Saman Payvar, Mir Khan, Rafael Stahl, Daniel Mueller-Gritschneder, J. Boutellier

{"title":"Neural Network-based Vehicle Image Classification for IoT Devices","authors":"Saman Payvar, Mir Khan, Rafael Stahl, Daniel Mueller-Gritschneder, J. Boutellier","doi":"10.1109/SiPS47522.2019.9020464","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020464","url":null,"abstract":"Convolutional Neural Networks (CNNs) have previously provided unforeseen results in automatic image analysis and interpretation, an area which has numerous applications in both consumer electronics and industry. However, the signal processing related to CNNs is computationally very demanding, which has prohibited their use in the smallest embedded computing platforms, to which many Internet of Things (IoT) devices belong. Fortunately, in the recent years researchers have developed many approaches for optimizing the performance and for shrinking the memory footprint of CNNs. This paper presents a neural-network-based image classifier that has been trained to classify vehicle images into four different classes. The neural network is optimized by a technique called binarization, and the resulting binarized network is placed to an IoT-class processor core for execution. Binarization reduces the memory footprint of the CNN by around 95% and increases performance by more than $6 times $. Furthermore, we show that by utilizing a custom instruction ’popcount’ of the processor, the performance of the binarized vehicle classifier can still be increased by more than $2 times $, making the CNN-based image classifier suitable for the smallest embedded processors.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"7 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126140675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Feature Selection Framework for XGBoost Based on Electrodermal Activity in Stress Detection 应力检测中基于皮肤电活动的XGBoost特征选择框架

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-10-01 DOI: 10.1109/SiPS47522.2019.9020321

Cheng-Ping Hsieh, Yi-Ta Chen, Win-Ken Beh, A. Wu

引用次数: 27