2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)最新文献

Neuromorphic networks on the SpiNNaker platform SpiNNaker平台的神经形态网络

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771512

G. Haessig, F. Galluppi, Xavier Lagorce, R. Benosman

引用次数: 7

Robust Learning and Recognition of Visual Patterns in Neuromorphic Electronic Agents 神经形态电子代理视觉模式的鲁棒学习和识别

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771580

Dongchen Liang, Raphaela Kreiser, Carsten Nielsen, Ning Qiao, Yulia Sandamirskaya, G. Indiveri

{"title":"Robust Learning and Recognition of Visual Patterns in Neuromorphic Electronic Agents","authors":"Dongchen Liang, Raphaela Kreiser, Carsten Nielsen, Ning Qiao, Yulia Sandamirskaya, G. Indiveri","doi":"10.1109/AICAS.2019.8771580","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771580","url":null,"abstract":"Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fast and power-efficient computing. However, the unavoidable variance inherently existing in the analog circuits makes it challenging to develop neural processing architectures able to perform complex computations robustly. In this paper, we present a spiking neural network architecture with spike-based learning that enables robust learning and recognition of visual patterns in noisy silicon neural substrate and noisy environments. The architecture is used to perform pattern recognition and inference after a training phase with computers and neuromorphic hardware in the loop. We validate the proposed system in a closed-loop hardware setup composed of neuromorphic vision sensors and processors, and we present experimental results that quantify its real-time and robust perception and action behavior.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961045","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: 2

Low Precision Electroencephalogram for Seizure Detection with Convolutional Neural Network 低精度脑电图卷积神经网络检测癫痫发作

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771569

N. D. Truong, O. Kavehei

{"title":"Low Precision Electroencephalogram for Seizure Detection with Convolutional Neural Network","authors":"N. D. Truong, O. Kavehei","doi":"10.1109/AICAS.2019.8771569","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771569","url":null,"abstract":"Electroencephalogram (EEG) neural activity recording has been widely used for diagnosing and monitoring epileptic patients. Ambulatory epileptic monitoring devices that can detect or even predict seizures play an important role for patients with intractable epilepsy. Though many EEG-based seizure detection algorithms have been proposed in the literature with high accuracy, their hardware implementations are constrained because of power consumption. Many commercial non-research EEG monitoring systems samples multiple electrodes at a relatively high rate and transmit the data either via a wire or wirelessly to an external signal processing unit. In this work, we studied how a reduced sampling precision affects the performance of our machine learning signal processing in seizure detection. To answer this question, we reduce the number of bits (precision) in an analog-to-digital converter (ADC) used in an EEG recorder. The outcome shows that the reduction of ADC precision down to 6-bit does not significantly reduce our convolutional neural network performance in detecting seizure onsets. As an indication of the performance, we achieved an area under the curve (AUC) more than 92% and above 96% on Freiburg Hospital and the Boston Children’s Hospital-MIT seizure datasets, respectively. A possible reduction in ADC precision not only contribute to energy consumption reduction, particularly if the data has to be transmitted, but also offers an improved computational efficacy regarding memory requirement and circuit area.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997786","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: 5

Deep Multi-Scale Residual Learning-based Blocking Artifacts Reduction for Compressed Images 基于深度多尺度残差学习的压缩图像块伪影减少

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771613

Min-Hui Lin, C. Yeh, Chu-Han Lin, Chih-Hsiang Huang, Li-Wei Kang

{"title":"Deep Multi-Scale Residual Learning-based Blocking Artifacts Reduction for Compressed Images","authors":"Min-Hui Lin, C. Yeh, Chu-Han Lin, Chih-Hsiang Huang, Li-Wei Kang","doi":"10.1109/AICAS.2019.8771613","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771613","url":null,"abstract":"Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a general problem in block-based image/video compression systems. Various post-processing techniques have been proposed to reduce blocking artifacts, but most of them usually introduce excessive blurring or ringing effects. This paper presents a deep learning-based compression artifacts reduction (or deblocking) framework relying on multi-scale residual learning. Recent popular approaches usually train deep models using a per-pixel loss function with explicit image priors for directly producing deblocked images. Instead, we formulate the problem as learning the residuals (or the artifacts) between original and the corresponding compressed images. In our deep model, each input image is down-scaled first with blocking artifacts naturally reduced. Then, the learned SR (super-resolution) convolutional neural network (CNN) will be used to up-sample the down-scaled version. Finally, the up-scaled version (with less artifacts) and the original input are fed into the learned artifact prediction CNN to obtain the estimated blocking artifacts. As a result, the blocking artifacts can be successfully removed by subtracting the predicted artifacts from the input image while preserving most original visual details.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133689851","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

A Flexible and High-Performance Self-Organizing Feature Map Training Acceleration Circuit and Its Applications 一种灵活、高性能的自组织特征映射训练加速电路及其应用

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771556

Yuheng Sun, T. Chiueh

引用次数: 1

Complexity Reduction on HEVC Intra Mode Decision with modified LeNet-5 基于改进LeNet-5的HEVC模式内决策复杂度降低

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771586

Hai-Che Ting, H. Fang, Jia-Shung Wang

{"title":"Complexity Reduction on HEVC Intra Mode Decision with modified LeNet-5","authors":"Hai-Che Ting, H. Fang, Jia-Shung Wang","doi":"10.1109/AICAS.2019.8771586","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771586","url":null,"abstract":"The HEVC (H.265) standard was finalized in April 2013, currently being as the prevalent video coding standard. One key contributor to the performance gain over H.264 is the intra prediction that extended a large number of prediction directions on various sizes of prediction units (PUs), thus at a cost of very high computational complexity. When HEVC has been emerged, several fast Intra prediction and Coding Unit (CU) size decision algorithms are being developed for practical applications. Actually, these two components would cost around 60% to 70% encoding time in the all-intra HEVC encoding. In this paper, a novel CNN-based solution is proposed and evaluated. The main idea is to elect a smallest set of adequate intra directions using our modified LeNet-5 CNN model, thus reduce the computational complexity of (further) rate distortion optimization to a tolerable limit. Besides, two filters are employed: the edge strength extractor in [4] and the early terminated CU partition in [7] to skip most of the unlikely directions and to decrease the number of CUs, respectively. The experimental results demonstrate that the proposed method provides a decrease of up to 66.59% computation with a slightly increase in the bit-rate (1.1% on average) and a little reduction of picture quality (0.109% on average in PSNR) at most.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121089562","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: 6

Fast Convolution Algorithm for Convolutional Neural Networks 卷积神经网络的快速卷积算法

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771531

Tae-Sun Kim, Ji-Hoon Bae, M. Sunwoo

引用次数: 3

Memristor Emulators for an Adaptive DPE Algorithm: Comparative Study 自适应DPE算法的忆阻器仿真器:比较研究

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771594

Hussein Assaf, Y. Savaria, M. Sawan

引用次数: 2

On-chip Learning of Multilayer Perceptron Based on Memristors with Limited Multilevel States 基于有限多能级状态忆阻器的多层感知器片上学习

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771513

Yuhang Zhang, Guanghui He, K. Tang, Guoxing Wang

引用次数: 5

Artificial Intelligence of Things Wearable System for Cardiac Disease Detection 用于心脏病检测的物联网可穿戴系统

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-18 DOI: 10.1109/AICAS.2019.8771630

Yu-Jin Lin, Chen-Wei Chuang, Chun-Yueh Yen, Sheng-Hsin Huang, Peng-Wei Huang, Ju-Yi Chen, Shuenn-Yuh Lee

{"title":"Artificial Intelligence of Things Wearable System for Cardiac Disease Detection","authors":"Yu-Jin Lin, Chen-Wei Chuang, Chun-Yueh Yen, Sheng-Hsin Huang, Peng-Wei Huang, Ju-Yi Chen, Shuenn-Yuh Lee","doi":"10.1109/AICAS.2019.8771630","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771630","url":null,"abstract":"This study proposes an artificial intelligence of things (AIoT) system for electrocardiogram (ECG) analysis and cardiac disease detection. The system includes a front-end IoT-based hardware, a user interface on smart device’s application (APP), a cloud database, and an AI platform for cardiac disease detection. The front-end IoT-based hardware, a wearable ECG patch that includes an analog front-end circuit and a Bluetooth module, can detect ECG signals. The APP on smart devices can not only display users’ real-time ECG signals but also label unusual signals instantly and reach real-time disease detection. These ECG signals will be uploaded to the cloud database. The cloud database is used to store each user’s ECG signals, which forms a big-data database for AI algorithm to detect cardiac disease. The algorithm proposed by this study is based on convolutional neural network and the average accuracy is 94.96%. The ECG dataset applied in this study is collected from patients in Tainan Hospital, Ministry of Health and Welfare. Moreover, signal verification was also performed by a cardiologist.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342928","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: 16