Proceedings of the 1st Workshop on Machine Learning and Systems最新文献_第2页

DISC: A Dynamic Shape Compiler for Machine Learning Workloads DISC:用于机器学习工作负载的动态形状编译器

Proceedings of the 1st Workshop on Machine Learning and Systems Pub Date : 2021-03-09 DOI: 10.1145/3437984.3458838

Kai Zhu, Wenyi Zhao, Zhen Zheng, Tianyou Guo, Pengzhan Zhao, Junjie Bai, Jun Yang, Xiaoyong Liu, Lansong Diao, Wei Lin

引用次数: 14

Towards a General Framework for ML-based Self-tuning Databases 基于机器学习的自调优数据库通用框架研究

Proceedings of the 1st Workshop on Machine Learning and Systems Pub Date : 2020-11-16 DOI: 10.1145/3437984.3458830

Thomas Schmied, Diego Didona, Andreas Doring, Thomas Parnell, Nikolas Ioannou

{"title":"Towards a General Framework for ML-based Self-tuning Databases","authors":"Thomas Schmied, Diego Didona, Andreas Doring, Thomas Parnell, Nikolas Ioannou","doi":"10.1145/3437984.3458830","DOIUrl":"https://doi.org/10.1145/3437984.3458830","url":null,"abstract":"Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimization (BO) and reinforcement learning (RL). In this work, we describe our experience when applying these methods to a database not yet studied in this context: FoundationDB. Firstly, we describe the challenges we faced, such as unknown valid ranges of configuration parameters and combinations of parameter values that result in invalid runs, and how we mitigated them. While these issues are typically overlooked, we argue that they are a crucial barrier to the adoption of ML self-tuning techniques in databases, and thus deserve more attention from the research community. Secondly, we present experimental results obtained when tuning FoundationDB using ML methods. Unlike prior work in this domain, we also compare with the simplest of baselines: random search. Our results show that, while BO and RL methods can improve the throughput of FoundationDB by up to 38%, random search is a highly competitive baseline, finding a configuration that is only 4% worse than the, vastly more complex, ML methods. We conclude that future work in this area may want to focus more on randomized, model-free optimization algorithms.","PeriodicalId":269840,"journal":{"name":"Proceedings of the 1st Workshop on Machine Learning and Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130262656","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: 8

μNAS: Constrained Neural Architecture Search for Microcontrollers μNAS:微控制器约束神经结构搜索

Proceedings of the 1st Workshop on Machine Learning and Systems Pub Date : 2020-10-27 DOI: 10.1145/3437984.3458836

Edgar Liberis, L. Dudziak, N. Lane

{"title":"μNAS: Constrained Neural Architecture Search for Microcontrollers","authors":"Edgar Liberis, L. Dudziak, N. Lane","doi":"10.1145/3437984.3458836","DOIUrl":"https://doi.org/10.1145/3437984.3458836","url":null,"abstract":"IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage. Designing neural networks for such a platform requires an intricate balance between keeping high predictive performance (accuracy) while achieving low memory and storage usage and inference latency. This is extremely challenging to achieve manually, so in this work, we build a neural architecture search (NAS) system, called μNAS, to automate the design of such small-yet-powerful MCU-level networks. μNAS explicitly targets the three primary aspects of resource scarcity of MCUs: the size of RAM, persistent storage and processor speed. μNAS represents a significant advance in resource-efficient models, especially for \"mid-tier\" MCUs with memory requirements ranging from 0.5 KB to 64 KB. We show that on a variety of image classification datasets μNAS is able to (a) improve top-1 classification accuracy by up to 4.8%, or (b) reduce memory footprint by 4-13×, or (c) reduce the number of multiply-accumulate operations by at least 2×, compared to existing MCU specialist literature and resource-efficient models. μNAS is freely available for download at https://github.com/eliberis/uNAS","PeriodicalId":269840,"journal":{"name":"Proceedings of the 1st Workshop on Machine Learning and Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128275359","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: 66

DPD-InfoGAN: Differentially Private Distributed InfoGAN DPD-InfoGAN:差分私有分布式信息网络

Proceedings of the 1st Workshop on Machine Learning and Systems Pub Date : 2020-10-22 DOI: 10.1145/3437984.3458826

Vaikkunth Mugunthan, V. Gokul, Lalana Kagal, S. Dubnov

{"title":"DPD-InfoGAN: Differentially Private Distributed InfoGAN","authors":"Vaikkunth Mugunthan, V. Gokul, Lalana Kagal, S. Dubnov","doi":"10.1145/3437984.3458826","DOIUrl":"https://doi.org/10.1145/3437984.3458826","url":null,"abstract":"Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacy-preserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy.","PeriodicalId":269840,"journal":{"name":"Proceedings of the 1st Workshop on Machine Learning and Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122147631","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: 8

AutoAblation AutoAblation

Proceedings of the 1st Workshop on Machine Learning and Systems Pub Date : 1900-01-01 DOI: 10.1145/3437984.3458834

Sina Sheikholeslami, Moritz Meister, Tianze Wang, A. H. Payberah, Vladimir Vlassov, J. Dowling

引用次数: 22

Vate Vate

Proceedings of the 1st Workshop on Machine Learning and Systems Pub Date : 1900-01-01 DOI: 10.1145/3437984.3458835

D. Goodman, A. Pocock, Jason Peck, Guy L. Steele

引用次数: 0