2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)最新文献_第3页

Optimizations of Scatter Network for Sparse CNN Accelerators 稀疏CNN加速器的散射网络优化

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771480

Sunwoo Kim, Chung-Mok Lee, Haesung Park, Jooho Wang, Sungkyung Park, C. Park

引用次数: 3

SMURFF: a High-Performance Framework for Matrix Factorization SMURFF:一个高性能的矩阵分解框架

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771607

T. Aa, Imen Chakroun, Thomas J. Ashby

引用次数: 5

A CMOS-based Resistive Crossbar Array with Pulsed Neural Network for Deep Learning Accelerator

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771576

Injune Yeo, Sang-gyun Gi, Jung-gyun Kim, Byung-geun Lee

引用次数: 0

DropOut and DropConnect for Reliable Neuromorphic Inference under Energy and Bandwidth Constraints in Network Connectivity 网络连接中能量和带宽约束下可靠神经形态推理的DropOut和DropConnect

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771533

Yasufumi Sakai, B. Pedroni, Siddharth Joshi, Abraham Akinin, G. Cauwenberghs

{"title":"DropOut and DropConnect for Reliable Neuromorphic Inference under Energy and Bandwidth Constraints in Network Connectivity","authors":"Yasufumi Sakai, B. Pedroni, Siddharth Joshi, Abraham Akinin, G. Cauwenberghs","doi":"10.1109/AICAS.2019.8771533","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771533","url":null,"abstract":"DropOut and DropConnect are known as effective methods to improve on the generalization performance of neural networks, by either dropping states of neural units or dropping weights of synaptic connections randomly selected at each time instance throughout the training process. In this paper, we extend on the use of these methods in the design of neuromorphic spiking neural networks (SNN) hardware to improve further on the reliability of inference as impacted by resource constrained errors in network connectivity. Such energy and bandwidth constraints arise for low-power operation in the communication between neural units, which cause dropped spike events due to timeout errors in the transmission. The DropOut and DropConnect processes during training of the network are aligned with a statistical model of the network during inference that accounts for these random errors in the transmission of neural states and synaptic connections. The use of DropOut and DropConnect during training hence allows to simultaneously meet two design objectives: maximizing bandwidth, while minimizing energy of inference in neuromorphic hardware. Simulations of the model with a 5-layer fully connected 784-500-500-500-10 SNN on the MNIST task show a 5-fold and 10-fold improvement in bandwidth during inference at greater than 98% accuracy, using DropOut and DropConnect respectively during backpropagation training.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116883116","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

Modern Architecture Style Transfer for Ruin Buildings 废墟建筑的现代建筑风格转换

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771623

Chia-Ching Wang, Hsin-Hua Liu, S. Pei, Kuan-Hsien Liu, Tsung-Jung Liu

引用次数: 4

On Automatic Generation of Training Images for Machine Learning in Automotive Applications 汽车应用中机器学习训练图像的自动生成

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771605

Tong-Yu Hsieh, Yuan-Cheng Lin, Hsin-Yung Shen

{"title":"On Automatic Generation of Training Images for Machine Learning in Automotive Applications","authors":"Tong-Yu Hsieh, Yuan-Cheng Lin, Hsin-Yung Shen","doi":"10.1109/AICAS.2019.8771605","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771605","url":null,"abstract":"Machine learning is expected to play an important role in implementing automotive systems such as the Advanced Driver Assistance Systems (ADAS). To make machine learning methods work well, providing a sufficient number of training data is very important. However, collecting the training data may be difficult or very timing-consuming. In this paper we investigate automatic generation of training data for automotive applications. The Generative Adversarial Network (GAN) techniques are employed to generate fake yet still high-quality data for machine learning. Although using GAN to generate training images has been proposed in the literature, the previous work does not consider automotive applications. In this work a case study on vehicle detection is provided to demonstrate powerfulness of GAN and the effectiveness of the generated training images by GAN. The generated fake bus images are employed as training data and a SVM (Support Vector Machine) method is implemented to detect buses. The results show that the SVM trained by the fake images achieves almost the same detection accuracy as that by real images. The result also shows that GAN can generate the training images very fast. The extension of GAN to generate road images with various weather conditions such as fogs or nights is also discussed.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121538888","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

Multi-level Weight Indexing Scheme for Memory-Reduced Convolutional Neural Network 记忆缩减卷积神经网络的多级权重索引方案

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771492

Jongmin Park, Seungsik Moon, Younghoon Byun, Sunggu Lee, Youngjoo Lee

引用次数: 2

A Learnable Unmanned Smart Logistics Prototype System Design and Implementation 可学习无人智能物流原型系统设计与实现

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771589

I-Lok Cheng, Ching-Hwa Cheng, Don-Gey Liu

引用次数: 3

Configurable Texture Unit for Convolutional Neural Networks on Graphics Processing Units 图形处理单元上卷积神经网络的可配置纹理单元

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771629

Yi-Hsiang Chen, Shao-Yi Chien

引用次数: 1

Reconfigurable Edge via Analytics Architecture 通过分析架构可重新配置边缘

2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2019-03-01 DOI: 10.1109/AICAS.2019.8771528

Shih-Yu Chen, G. Lee, Tai-Ping Wang, Chin-Wei Huang, Jia-Hong Chen, Chang-Ling Tsai

{"title":"Reconfigurable Edge via Analytics Architecture","authors":"Shih-Yu Chen, G. Lee, Tai-Ping Wang, Chin-Wei Huang, Jia-Hong Chen, Chang-Ling Tsai","doi":"10.1109/AICAS.2019.8771528","DOIUrl":"https://doi.org/10.1109/AICAS.2019.8771528","url":null,"abstract":"As artificial intelligence (AI) algorithms requiring high accuracy become exceedingly more complex and Edge/IoT generated data becomes increasingly bigger, flexible reconfigurable processing is crucial in the design of efficient smart edge systems requiring low power and is introduced in this paper. In AI, analytics algorithms are typically used to analyze speech, audio, image video data, etc. In current cross-level system design methodology different algorithmic realizations are analyzed in the form of dataflow graphs (DFG) to further increase efficiency and flexibility in constituting “analytics architecture”. Having information on both algorithmic behavior and architectural information including software and hardware, the DFG so introduced provides a mathematical representation which, as opposed to traditional linear difference equations, better models the underlying computational platform for systematic analysis thus providing flexible and efficient management of the computational and storage resources. In our analytics architecture work, parallel and reconfigurable computing are formulated via DFG which are analogous to the analysis and synthesis equations of the well-known Fourier transform pair. In parallel computing, a connected component is eigen-decomposed to unconnected components for concurrent processing. For computation resource saving, commonalities in DFGs are analyzed for reuse when synthesizing or reconfiguring the edge platform. In this paper, we specifically introduce lightweight edge upon which algorithmic convolution for Convolution Neural Network are eigen-transformed to matrix operations with higher symmetry which facilitates fewer operations, lower data transfer rate and storage anticipating lower power when synthesizing or reconfiguring the eigenvectors.","PeriodicalId":273095,"journal":{"name":"2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123468957","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