2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)最新文献_第2页

Using Deep Neural Networks And Derivative Free Optimization To Accelerate Coverage Closure 利用深度神经网络和无导数优化加速覆盖闭合

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531234

Raviv Gal, E. Haber, Brian Irwin, Marwa Mouallem, Bilal Saleh, A. Ziv

引用次数: 1

On the Effectiveness of Quantization and Pruning on the Performance of FPGAs-based NN Temperature Estimation 量化和剪枝对基于fpga的神经网络温度估计性能的影响

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531256

V. V. R. M. K. Muvva, Martin Rapp, J. Henkel, H. Amrouch, M. Wolf

{"title":"On the Effectiveness of Quantization and Pruning on the Performance of FPGAs-based NN Temperature Estimation","authors":"V. V. R. M. K. Muvva, Martin Rapp, J. Henkel, H. Amrouch, M. Wolf","doi":"10.1109/MLCAD52597.2021.9531256","DOIUrl":"https://doi.org/10.1109/MLCAD52597.2021.9531256","url":null,"abstract":"A well-functioning thermal management system on the chip requires knowledge of the current temperature and the potential changes in temperature in the near future. This information is important for ensuring proactive thermal management on the chip. However, the limited number of sensors on the chip makes it difficult to accomplish this task. Hence we proposed a neural network based approach to predict the temperature map of the chip. To solve the problem, we have implemented two different neural networks, one is a feedforward network and the other uses recurrent neural networks. Our proposed method requires only performance counters measure to predict the temperature map of the chip during the runtime. Each of the two models shows promising results regarding the estimation of the temperature map on the chip. The recurrent neural network outperformed the feedforward neural network. Furthermore, both networks have been quantized, pruned, and the feedforward network has been compiled into FPGA logic. Therefore, the network could be embedded in the chip, whether it be an ASIC or an FPGA.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125643447","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

Ensemble Learning Based Electric Components Footprint Analysis 基于集成学习的电子元件足迹分析

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531219

Peng Huang, Xuan-Yi Lin, Yan-Jhih Wang, Tsung-Yi Ho

引用次数: 1

Learning-Based Workload Phase Classification and Prediction Using Performance Monitoring Counters 基于学习的工作负荷阶段分类和性能监控计数器预测

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531161

Erika S. Alcorta, A. Gerstlauer

{"title":"Learning-Based Workload Phase Classification and Prediction Using Performance Monitoring Counters","authors":"Erika S. Alcorta, A. Gerstlauer","doi":"10.1109/MLCAD52597.2021.9531161","DOIUrl":"https://doi.org/10.1109/MLCAD52597.2021.9531161","url":null,"abstract":"Predicting coarse-grain variations in workload behavior during execution is essential for dynamic resource optimization of processor systems. Researchers have proposed various methods to first classify workloads into phases and then learn their long-term phase behavior to predict and anticipate phase changes. Early studies on phase prediction proposed table-based phase predictors. More recently, simple learning-based techniques such as decision trees have been explored. However, more recent advances in machine learning have not been applied to phase prediction so far. Furthermore, existing phase predictors have been studied only in connection with specific phase classifiers even though there is a wide range of classification methods. Early work in phase classification proposed various clustering methods that required access to source code. Some later studies used performance monitoring counters, but they only evaluated classifiers for specific contexts such as thermal modeling.In this work, we perform a comprehensive study of source-oblivious phase classification and prediction methods using hardware counters. We adapt classification techniques that were used with different inputs in the past and compare them to state-of-the-art hardware counter based classifiers. We further evaluate the accuracy of various phase predictors when coupled with different phase classifiers and evaluate a range of advanced machine learning techniques, including SVMs and LSTMs for workload phase prediction. We apply classification and prediction approaches to SPEC workloads running on an Intel Core-i9 platform. Results show that a two-level kmeans clustering combined with SVM-based phase change prediction provides the best tradeoff between accuracy and long-term stability. Additionally, the SVM predictor reduces the average prediction error by 80% when compared to a table-based predictor.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125597775","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

Learning based Memory Interference Prediction for Co-running Applications on Multi-Cores 基于学习的多核协同运行应用内存干扰预测

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531245

Ahsan Saeed, Daniel Mueller-Gritschneder, Falk Rehm, A. Hamann, D. Ziegenbein, Ulf Schlichtmann, A. Gerstlauer

{"title":"Learning based Memory Interference Prediction for Co-running Applications on Multi-Cores","authors":"Ahsan Saeed, Daniel Mueller-Gritschneder, Falk Rehm, A. Hamann, D. Ziegenbein, Ulf Schlichtmann, A. Gerstlauer","doi":"10.1109/MLCAD52597.2021.9531245","DOIUrl":"https://doi.org/10.1109/MLCAD52597.2021.9531245","url":null,"abstract":"Early run-time prediction of co-running independent applications prior to application integration becomes challenging in multi-core processors. One of the most notable causes is the interference at the main memory subsystem, which results in significant degradation in application performance and response time in comparison to standalone execution. Currently available techniques for run-time prediction like traditional cycle-accurate simulations are slow, and analytical models are not accurate and time-consuming to build. By contrast, existing machine-learning-based approaches for run-time prediction simply do not account for interference. In this paper, we use a machine learning-based approach to train a model to correlate performance data (instructions and hardware performance counters) for a set of benchmark applications between the standalone and interference scenarios. After that, the trained model is used to predict the run-time of co-running applications in interference scenarios. In general, there is no straightforward one-to-one correspondence between samples obtained from the standalone and interference scenarios due to the different run-times, i.e. execution speeds. To address this, we developed a simple yet effective sample alignment algorithm, which is a key component in transforming interference prediction into a machine learning problem. In addition, we systematically identify the subset of features that have the highest positive impact on the model performance. Our approach is demonstrated to be effective and shows an average run-time prediction error, which is as low as 0.3% and 0.1% for two co-running applications.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123087758","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

Massive Figure Extraction and Classification in Electronic Component Datasheets for Accelerating PCB Design Preparation 电子元件数据表中海量图形的提取与分类，加速PCB设计准备

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531275

Kuan-Chun Chen, Chou-Chen Lee, Mark Po-Hung Lin, Yan-Jhih Wang, Yi-Ting Chen

引用次数: 1

Delving into Macro Placement with Reinforcement Learning 用强化学习深入研究宏观布局

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531313

Zixuan Jiang, Ebrahim M. Songhori, Shen Wang, Anna Goldie, Azalia Mirhoseini, J. Jiang, Young-Joon Lee, David Z. Pan

引用次数: 7

A Survey of Graph Neural Networks for Electronic Design Automation 图神经网络在电子设计自动化中的应用综述

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531070

Daniela Sánchez Lopera, Lorenzo Servadei, Gamze Naz Kiprit, Souvik Hazra, R. Wille, W. Ecker

引用次数: 27

Domain-Adaptive Soft Real-Time Hybrid Application Mapping for MPSoCs 面向mpsoc的域自适应软实时混合应用映射

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531269

J. Spieck, S. Wildermann, Jürgen Teich

{"title":"Domain-Adaptive Soft Real-Time Hybrid Application Mapping for MPSoCs","authors":"J. Spieck, S. Wildermann, Jürgen Teich","doi":"10.1109/MLCAD52597.2021.9531269","DOIUrl":"https://doi.org/10.1109/MLCAD52597.2021.9531269","url":null,"abstract":"The mapping of soft real-time applications onto heterogeneous MPSoC architectures can have a high influence on execution properties like energy consumption or the number of deadline violations. In recent years, scenario-aware hybrid application mapping (HAM) has turned out as the state-of-the-art mapping method for input-dependent applications whose execution characteristics are in strong dependence on the input that shall be processed. In this work, we propose an extension of scenario-aware HAM that is capable of transferring its mapping strategy learned from a labeled source data domain using supervised learning into an unlabeled target domain that exhibits a shift in its data distribution. Our domain-adaptive HAM employs a run-time manager (RTM) that performs mapping selection and reconfiguration at run time based on general domain-invariant knowledge learned at design time that is valid for both the source and target domain. Evaluation based on two input-dependent applications and two MPSoC architectures demonstrates that our domain-adaptive HAM consistently outperforms state-of-the-art mapping procedures with regard to the number of deadline misses and energy consumption in presence of a domain shift. Furthermore, our HAM approach obtains results close to an explicit optimization for the target domain in a fraction of the necessary optimization time and without necessitating target labels.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124568856","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

Feeding Hungry Models Less: Deep Transfer Learning for Embedded Memory PPA Models : Special Session 少喂饥饿模型:嵌入式记忆PPA模型的深度迁移学习:特别会议

2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD) Pub Date : 2021-08-30 DOI: 10.1109/MLCAD52597.2021.9531299

F. Last, Ulf Schlichtmann

{"title":"Feeding Hungry Models Less: Deep Transfer Learning for Embedded Memory PPA Models : Special Session","authors":"F. Last, Ulf Schlichtmann","doi":"10.1109/MLCAD52597.2021.9531299","DOIUrl":"https://doi.org/10.1109/MLCAD52597.2021.9531299","url":null,"abstract":"Supervised machine learning requires large amounts of labeled data for training. In power, performance and area (PPA) estimation of embedded memories, every new memory compiler version is considered independently of previous versions. Since the data of different memory compilers originate from similar domains, transfer learning may reduce the amount of supervised data required by pre-training PPA estimation neural networks on related domains. We show that provisioning times of PPA models for new compiler versions can be reduced significantly by exploiting similarities across versions and technology nodes. Through transfer learning, we shorten the time to provision PPA models for new compiler versions by 50% to 90%, which speeds up time-critical periods of the design cycle. This is achieved by requiring less than 6,500 ground truth samples for the target compiler to achieve average estimation errors of 0.35% instead of 13,000 samples. Using only 1,300 samples is sufficient to achieve an almost worst-case (98th percentile) error of approximately 3% and allows us to shorten model provisioning times from over 40 days to less than one week.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127555042","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