2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2)最新文献

Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit 嵌入式设备上的深度学习推理:定点vs定点

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-05-22 DOI: 10.1109/EMC2.2018.00012

Seyed Hamed Fatemi Langroudi, Tej Pandit, D. Kudithipudi

引用次数: 36

Event Prediction in Processors Using Deep Temporal Models 基于深度时间模型的处理器事件预测

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-25 DOI: 10.1109/EMC2.2018.00014

Tharindu Mathew, Aswin Raghavan, S. Chai

引用次数: 0

A High Efficiency Accelerator for Deep Neural Networks 一种高效的深度神经网络加速器

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00010

Aliasger Zaidy, Andre Xian Ming Chang, Vinayak Gokhale, E. Culurciello

引用次数: 0

A Quantization-Friendly Separable Convolution for MobileNets 一种量化友好的移动网络可分离卷积

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00011

Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, M. Aleksic

引用次数: 101

A Case for Dynamic Activation Quantization in CNNs cnn中动态激活量化的一个例子

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00009

Karl Taht, Surya Narayanan, R. Balasubramonian

{"title":"A Case for Dynamic Activation Quantization in CNNs","authors":"Karl Taht, Surya Narayanan, R. Balasubramonian","doi":"10.1109/EMC2.2018.00009","DOIUrl":"https://doi.org/10.1109/EMC2.2018.00009","url":null,"abstract":"It is a well-established fact that CNNs are robust enough to tolerate low precision computations without any significant loss in accuracy. There have been works that exploit this fact, and try to allocate different precision for different layers (for both weights and activations), depending on the importance of a layer's precision in dictating the prediction accuracy. In all these works, the layer-wise precision of weights and activations is decided for a network by performing an offline design space exploration as well as retraining of weights. While these approaches show significant energy improvements, they make global decisions for precision requirements. In this project, we try to answer the question \"Can we vary the inter-and intra-layer bit-precision based on the region-wise importance of the individual input?\". The intuition behind this is that for a particular image, there might be regions that can be considered as background or unimportant for the network to make its final prediction. As these inputs propagate through the network, the regions of less importance in the same feature map can tolerate lower precision. Using metrics such as entropy, color gradient, and points of interest, we argue that a region of an image can be labeled important or unimportant, thus enabling lower precision for unimportant pixels. We show that per-input activation quantization can reduce computational energy up to 33.5% or 42.0% while maintaining original Top-1 and Top-5 accuracies respectively.","PeriodicalId":377872,"journal":{"name":"2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131624957","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

Invited Talk Abstract: Introducing ReQuEST: An Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments 摘要:介绍ReQuEST:一个可复制和高质量系统的开放平台- ml锦标赛

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/emc2.2018.00008

G. Fursin

{"title":"Invited Talk Abstract: Introducing ReQuEST: An Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments","authors":"G. Fursin","doi":"10.1109/emc2.2018.00008","DOIUrl":"https://doi.org/10.1109/emc2.2018.00008","url":null,"abstract":"Co-designing efficient machine learning based systems across the whole application/hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, heterogeneous hardware platforms, compilers, libraries, algorithms, data sets, models, and environments. I will present our community effort to develop an open co-design tournament platform with an online public scoreboard based on Collective Knowledge workflow framework (CK). It gradually incorporates best research practices while providing a common way for multidisciplinary researchers to optimize and compare the quality vs. efficiency Pareto optimality of various workloads on diverse and complete hardware/software systems. All the winning solutions will be made available to the community as portable and customizable \"plug&play\" components with a common API to accelerate research and innovation! I will then discuss how our open competition and collaboration can help to achieve energy efficiency for cognitive workloads based on energy-efficient submissions from the 1st ReQuEST tournament co-located with ASPLOS'18. Further details: http://cKnowledge.org/request","PeriodicalId":377872,"journal":{"name":"2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116544641","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: 1

Moving CNN Accelerator Computations Closer to Data 移动CNN加速器计算更接近数据

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00015

Sumanth Gudaparthi, Surya Narayanan, R. Balasubramonian

引用次数: 1

Keynote Abstract: Safety and Security at the Heart of Autonomous Driving 主题演讲摘要:安全与安保是自动驾驶的核心

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00006

K. Khouri

{"title":"Keynote Abstract: Safety and Security at the Heart of Autonomous Driving","authors":"K. Khouri","doi":"10.1109/EMC2.2018.00006","DOIUrl":"https://doi.org/10.1109/EMC2.2018.00006","url":null,"abstract":"The automotive industry is undergoing a revolution with connected, autonomous and electric vehicles and the benefits they can bring to the public. Drivers enjoying their daily commute, fewer road fatalities and less pollution are all possible thanks to new technologies. Car makers need to offer these features but at the same time make sure vehicles are safe and secure. In the coming years, there will be various levels of automation until we have fully autonomous vehicles. To achieve any level of automation, cars need to connect to other vehicles, connect to the infrastructure, sense the environment through various sensors such as camera and radar and then make maneuvering decisions based on all these inputs. Artificial intelligence is and will be deployed heavily to accomplish many of the tasks of autonomous driving. Perception and decision-making based on artificial intelligence introduces an entirely new set of challenges to car makers to ensure no security compromises as well as proving the decisions being made are functionally, behaviorally and environmentally safe. The challenge can be described in a simple question: \"If a machine learning based car system is accurate 99% of the time, are you willing to ride this car knowing that it will be wrong 1% of the time? What is the consequence of that incorrect decision?\" Deep expertise and research in the safety and security aspects of AI are needed to ensure future mass deployment and success in the area of autonomous driving.","PeriodicalId":377872,"journal":{"name":"2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126335145","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

Efficient Compiler Code Generation for Deep Learning Snowflake Co-Processor 深度学习雪花协处理器的高效编译代码生成

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00013

Andre Xian Ming Chang, Aliasger Zaidy, E. Culurciello

引用次数: 2

Invited Talk Abstract: Challenges and Solutions for Embedding Vision AI 摘要:嵌入视觉人工智能的挑战与解决方案

2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2) Pub Date : 2018-03-01 DOI: 10.1109/EMC2.2018.00007

Charles Qi

{"title":"Invited Talk Abstract: Challenges and Solutions for Embedding Vision AI","authors":"Charles Qi","doi":"10.1109/EMC2.2018.00007","DOIUrl":"https://doi.org/10.1109/EMC2.2018.00007","url":null,"abstract":"Recently computer vision and neural network based AI technology have seen explosive demands in embedded systems such as robots, drones, autonomous vehicles, etc. Due to cost and power constraints, it remains quite challenging to achieve satisfactory performance, while maintaining power efficiency and scalability for embedded vision AI. This presentation first analyzes the technical challenges of embedding vision AI, from the perspectives of algorithm complexity, computation and memory BW demands, and constrains of power consumption profile. The analysis shows that modern neural networks for vision AI contain complex topology and diversified computation steps. These neural networks are often part of a large embedded vision processing pipeline, intermixed with conventional vision algorithms. As a result, the vision AI implementation demands several TOPS computation performance and ten's of GB memory BW. Subsequently the architecture of Tensilica Vision AI DSP processor technology is presented with three distinctive advantages: The optimized instruction sets of Vision P6 and Vision C5 DSP are explained as examples of achieving instruction level computation efficiency and performance. This is coupled with unique processor architecture features for achieving SoC level data processing efficiency and scalability that lead to a high-performance vision AI sub-system. The fully automated AI optimization framework, software libraries and tools provide practical performance tuning methodology and rapid turn-around time for embedded vision AI system design. In conclusion, the presentation offers considerations for future research and development to bring embedded vision AI to the next performance level.","PeriodicalId":377872,"journal":{"name":"2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131134768","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