2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC)最新文献

A Low-Cost Stochastic Computing-based Fuzzy Filtering for Image Noise Reduction 一种低成本的基于随机计算的图像降噪模糊滤波

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969358

Seyedeh Newsha Estiri, Amir Hossein Jalilvand, S. Naderi, M. Najafi, Mahdi Fazeli

引用次数: 1

Exploring Automatic Gym Workouts Recognition Locally on Wearable Resource-Constrained Devices 探索在可穿戴资源受限设备上的自动健身训练识别

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969370

Sizhen Bian, Xiaying Wang, T. Polonelli, M. Magno

{"title":"Exploring Automatic Gym Workouts Recognition Locally on Wearable Resource-Constrained Devices","authors":"Sizhen Bian, Xiaying Wang, T. Polonelli, M. Magno","doi":"10.1109/IGSC55832.2022.9969370","DOIUrl":"https://doi.org/10.1109/IGSC55832.2022.9969370","url":null,"abstract":"Automatic gym activity recognition on energy-and resource-constrained wearable devices removes the human-interaction requirement during intense gym sessions - like soft-touch tapping and swiping. This work presents a tiny and highly accurate residual convolutional neural network that runs in milliwatt microcontrollers for automatic workouts classification. We evaluated the inference performance of the deep model with quantization on three resource-constrained devices: two microcontrollers with ARM-Cortex M4 and M7 core from ST Microelectronics, and a GAP8 system on chip, which is an open-sourced, multi-core RISC-V computing platform from Green-Waves Technologies. Experimental results show an accuracy of up to 90.4% for eleven workouts recognition with full precision inference. The paper also presents the trade-off performance of the resource-constrained system. While keeping the recognition accuracy (88.1%) with minimal loss, each inference takes only 3.2 ms on GAP8, benefiting from the 8 RISC-V cluster cores. We measured that it features an execution time that is 18.9x and 6.5x faster than the Cortex-M4 and Cortex-M7 cores, showing the feasibility of real-time on-board workouts recognition based on the described data set with 20 Hz sampling rate. The energy consumed for each inference on GAP8 is 0.41 mJ compared to 5.17 mJ on Cortex-M4 and 8.07 mJ on Cortex-M7 with the maximum clock. It can lead to longer battery life when the system is battery-operated. We also introduced an open data set composed of fifty sessions of eleven gym workouts collected from ten subjects that is publicly available.","PeriodicalId":114200,"journal":{"name":"2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116840014","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: 7

Electrical Commissioning Owner's Project Requirements: A Template 电气调试业主的项目要求:模板

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969369

Brandon Hong, E. Thomason, Aditya M. Deshpande

{"title":"Electrical Commissioning Owner's Project Requirements: A Template","authors":"Brandon Hong, E. Thomason, Aditya M. Deshpande","doi":"10.1109/IGSC55832.2022.9969369","DOIUrl":"https://doi.org/10.1109/IGSC55832.2022.9969369","url":null,"abstract":"As the power demands of Supercomputers continue to grow, so does the demands of the electrical systems that support the infrastructure and building in which these Supercomputers reside. A typical new Supercomputer installation requires an upgrade to the design of the electrical system. As Supercomputers are refreshed roughly every 3 years which in turn drives electrical systems upgrades. The pre-design phase is critical for planning the installation of a new Supercomputer and requires documenting the overarching project purpose, goals, expectations, preferences, and limitations for the electrical systems, especially as the number of stakeholders increases. This Owner's Project Requirements (OPR) document then becomes guidance to the engineering and design teams for the development of the initial basis-of-design and subsequent construction documents. The electrical systems commissioning OPR provides a guideline for stakeholders to make sure that the electrical systems are well designed ‘up-front’ in the process of installation of a new Supercomputer. It also serves as a guiding checklist for the reader to use to inform their own generation of project guiding documents. This document will assist the owner and respective HPC infrastructure stakeholders in writing an OPR for the electrical systems supporting data centers or high-performance computing (HPC) facilities. This paper provides a template for developing an electrical system commissioning OPR. The template is sub-divided into sections that should be discussed and documented as part of the overall project requirements. The expectation is that this outline template forms a starting point for discussions for generating a guiding document for the commissioning of the electrical systems and standardizes the best practices and processes needed for the certification of the electrical commissioning of the HPC Supercomputers facilities.","PeriodicalId":114200,"journal":{"name":"2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126551697","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

Soft Cluster Powercap at SuperMUC-NG with EAR 带有EAR的supermu - ng软集群电源帽

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969360

J. Corbalán, Lluis Alonso, C. Navarrete, Carla Guillén

引用次数: 0

Unified Cross-Layer Cluster-Node Scheduling for Heterogeneous Datacenters 异构数据中心的统一跨层集群节点调度

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969366

Wenkai Guan, Cristinel Ababei

引用次数: 1

Optimal Launch Bound Selection in CPU-GPU Hybrid Graph Applications with Deep Learning 基于深度学习的CPU-GPU混合图形应用的最优启动边界选择

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969364

Md. Erfanul Haque Rafi, Apan Qasem

引用次数: 0

Energy-Performance-Security Trade-off in Mobile Edge Computing 移动边缘计算中的能源-性能-安全权衡

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969375

Mahipal P. Singh, S. Sankaran

引用次数: 0

A Review of Smart Buildings Protocol and Systems with a Consideration of Security and Energy Awareness 考虑安全和能源意识的智能建筑协议和系统综述

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969359

Mini Zeng

引用次数: 0

Raptor: Mitigating CPU-GPU False Sharing Under Unified Memory Systems 猛禽:减少统一内存系统下CPU-GPU错误共享

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969376

Md. Erfanul Haque Rafi, Kaylee Williams, Apan Qasem

{"title":"Raptor: Mitigating CPU-GPU False Sharing Under Unified Memory Systems","authors":"Md. Erfanul Haque Rafi, Kaylee Williams, Apan Qasem","doi":"10.1109/IGSC55832.2022.9969376","DOIUrl":"https://doi.org/10.1109/IGSC55832.2022.9969376","url":null,"abstract":"The introduction of Unified Memory (UM) technology has greatly increased the programmability of CPU-GPU heterogeneous systems. At the same time, Unified Memory systems have given rise to new performance challenges. Achieving the desired performance and energy efficiency on such systems requires careful consideration of data allocation and migration. This paper looks at the problem of false sharing under UM. We present Raptor, a system for fast and accurate detection of page-level false sharing in heterogeneous applications. The system employs binary code instrumentation and leverages hardware performance counters to track UM allocations and data access patterns and pinpoint energy inefficiencies created by the occurrence of false sharing. Experiments on a suite of heterogeneous applications show false sharing can be a common occurrence in collaborative design paradigms with tight coupling of CPU-GPU tasks. When false sharing is eliminated via a padding scheme, applications are able to achieve higher performance at lower clock frequencies, leading to improved energy efficiency by as much as 2.96× and by 1.62× and 1.47× on average on two contemporary CPU-GPU platforms.","PeriodicalId":114200,"journal":{"name":"2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128634426","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

MOSP: Multi-Objective Sensitivity Pruning of Deep Neural Networks 深度神经网络的多目标灵敏度剪枝

2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC) Pub Date : 2022-10-24 DOI: 10.1109/IGSC55832.2022.9969374

Muhammad Sabih, Ashutosh Mishra, Frank Hannig, Jürgen Teich

{"title":"MOSP: Multi-Objective Sensitivity Pruning of Deep Neural Networks","authors":"Muhammad Sabih, Ashutosh Mishra, Frank Hannig, Jürgen Teich","doi":"10.1109/IGSC55832.2022.9969374","DOIUrl":"https://doi.org/10.1109/IGSC55832.2022.9969374","url":null,"abstract":"Deep neural networks (DNNs) are computationally intensive, making them difficult to deploy on resource-constrained embedded systems. Model compression is a set of techniques that removes redundancies from a neural network with affordable degradation in task performance. Most compression methods do not target hardware-based objectives such as latency directly; however, few methods approximate latency with floating-point operations (FLOPs) or multiply-accumulate operations (MACs). Using these indirect metrics cannot directly translate to the relevant performance metric on the hardware, i.e., latency and throughput. To address this limitation, we introduce Multi-Objective Sensitivity Pruning, “MOSP,” a three-stage pipeline for filter pruning: hardware-aware sensitivity analysis, Criteria-optimal configuration selection, and pruning based on explainable AI (XAI). Our pipeline is compatible with a single or combination of target objectives such as latency, energy consumption, and accuracy. Our method first formulates the sensitivity of layers of a model against the target objectives as a classical machine learning problem. Next, we choose a Criteria-optimal configuration controlled by hyperparameters specific to each objective of choice. Finally, we apply XAI-based filter ranking to select filters to be pruned. The pipeline follows an iterative pruning methodology to recover any loss in degradation in task performance (e.g., accuracy). We allow the user to prefer one objective function over the other. Our method outperforms the selected baseline method across different neural networks and datasets in both accuracy and latency reductions and is competitive with state-of-the-art approaches.","PeriodicalId":114200,"journal":{"name":"2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127546916","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