Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion最新文献

A quantifiable approach to approximate computing: special session 近似计算的可量化方法:特殊会话

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125511

Chaofan Li, Deepashree Sengupta, F. S. Snigdha, Wenbin Xu, Jiang Hu, S. Sapatnekar

{"title":"A quantifiable approach to approximate computing: special session","authors":"Chaofan Li, Deepashree Sengupta, F. S. Snigdha, Wenbin Xu, Jiang Hu, S. Sapatnekar","doi":"10.1145/3125501.3125511","DOIUrl":"https://doi.org/10.1145/3125501.3125511","url":null,"abstract":"Approximate computing has applications in areas such as image processing, neural computation, distributed systems, and real-time systems, where the results may be acceptable in the presence of controlled levels of error. The promise of approximate computing is in its ability to render just enough performance to meet quality constraints. However, going from this theoretical promise to a practical implementation requires a clear comprehension of the system requirements and matching them to the design of approximations as the system is implemented. This involves the tasks of (a) identifying the design space of potential approximations, (b) modeling the injected error as a function of the level of approximation, and (c) optimizing the system over the design space to maximize a metric, typically the power savings, under constraints on the maximum allowable degradation. Often, the error may be introduced at a low level of design (e.g., at the level of a full adder) but its impact must be percolated up to system-level error metrics (e.g., PSNR in a compressed image), and a practical approach must devise a coherent and quantifiable way of translating between error/power tradeoffs at all levels of design.","PeriodicalId":259093,"journal":{"name":"Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122610546","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: 3

Advanced ahead-of-time compilation for Javascript engine: work-in-progress 高级提前编译Javascript引擎:工作在进行中

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125512

Hyukwoo Park, SungKook Kim, Soo-Mook Moon

引用次数: 4

Incremental training of CNNs for user customization: work-in-progress 针对用户定制的cnn增量训练:正在进行中

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125519

M. S. Moghaddam, B. Harris, Duseok Kang, Inpyo Bae, Euiseok Kim, Hyemi Min, Hansu Cho, Sukjin Kim, Bernhard Egger, S. Ha, Kiyoung Choi

引用次数: 0

Emerging (un-)reliability based security threats and mitigations for embedded systems: special session 嵌入式系统新出现的(非)基于可靠性的安全威胁和缓解:特别会议

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125529

H. Amrouch, P. Krishnamurthy, Naman Patel, J. Henkel, R. Karri, F. Khorrami

引用次数: 14

Probabilistic reasoning for analysis of approximate computations 近似计算分析的概率推理

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125524

Sasa Misailovic

{"title":"Probabilistic reasoning for analysis of approximate computations","authors":"Sasa Misailovic","doi":"10.1145/3125501.3125524","DOIUrl":"https://doi.org/10.1145/3125501.3125524","url":null,"abstract":"Popular application domains such as multimedia processing, machine learning, and big-data analytics operate on inherently noisy data and make decisions under uncertainty. While these applications are often good candidates for both algorithmic and system-level approximation, a major open challenge is how to analyze the influence of noisy data and candidate approximations on the application's outputs. At the same time, probabilistic programming languages provide an intuitive way to model uncertainty by expressing complex probabilistic models as computer programs. The talk will give an overview of PSI (http://www.psisolver.org), a system for exact symbolic inference. PSI computes succinct symbolic representations of the joint posterior distribution represented by a probabilistic program using static analysis. PSI supports programs with both discrete and continuous distributions. It can compute answers to various posterior distribution queries, expectation queries and assertion queries using its own back-end for symbolic reasoning. This talk will present how we can represent some problems in approximate computing as probabilistic programs and use PSI to automatically get symbolic expressions that represent the distributions of the output error.","PeriodicalId":259093,"journal":{"name":"Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130824237","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

Towards efficient quantized neural network inference on mobile devices: work-in-progress 在移动设备上实现高效量化神经网络推理:正在进行中

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125528

Yaman Umuroglu, Magnus Jahre

引用次数: 6

Balanced cache bypassing for critical warp reduction: work-in-progress 平衡缓存绕过关键翘曲减少:工作在进行中

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125513

Sungin Hong, Hyunjun Kim, Hwansoo Han

引用次数: 1

Code-size-aware mapping for synchronous dataflow graphs on multicore systems: work-in-progress 多核系统上同步数据流图的代码大小感知映射:正在进行的工作

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125514

Mingze Ma, R. Sakellariou

引用次数: 2

Towards industry strength mapping of AUTOSAR automotive functionality on multicore architectures: work-in-progress 在多核架构上实现AUTOSAR汽车功能的行业优势映射:正在进行中

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125623

Cosmin Avasalcai, Dhanesh Budhrani, P. Pop

{"title":"Towards industry strength mapping of AUTOSAR automotive functionality on multicore architectures: work-in-progress","authors":"Cosmin Avasalcai, Dhanesh Budhrani, P. Pop","doi":"10.1145/3125501.3125623","DOIUrl":"https://doi.org/10.1145/3125501.3125623","url":null,"abstract":"The automotive electronic architectures have moved from federated architectures, where one function is implemented in one ECU (Electronic Control Unit), to distributed architectures, consisting of several multicore ECUs. In addition, multicore ECUs are being adopted because of better performance, cost, size, fault-tolerance and power consumption. Automotive manufacturers use AUTomotive Open System ARchitecture (AUTOSAR) as the standardized software architecture for ECUs. With AUTOSAR, the functionality is modeled as a set of software components composed of subtasks, called runnables. In this paper we propose an approach for the automatic software functionality assignment to multicore distributed architectures, implemented as a software tool. The AUTOMAP, decides: the (i) mapping of software components to multicore ECUs, (ii) the assignment of runnables to the ECU cores, (iii) the clustering of runnables into tasks and (iv) the mapping of tasks to 'OS-Applications', such that timing and mapping constraints are satisfied. AUTOMAP has been developed to handle large industrialsized use cases, fine-grained realistic mapping and timing constraints, and to produce outputs that support the system engineer in the mapping task. We have successfully evaluated AUTOMAP on several realistic use cases from Volvo Trucks.","PeriodicalId":259093,"journal":{"name":"Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion","volume":"2645 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305334","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

Optimizing DCNN FPGA accelerator design for handwritten hangul character recognition: work-in-progress 优化手写韩文字符识别的DCNN FPGA加速器设计:正在进行中

Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion Pub Date : 2017-10-15 DOI: 10.1145/3125501.3125522

Hanwool Park, Changdae Lee, Hakkyung Lee, Yechan Yoo, Yoonjin Park, Injung Kim, Kang Yi

引用次数: 9