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Co-Designing NVM-based systems for Machine Learning and In-memory Search applications 24

Co-Designing NVM-based Systems for Machine Learning and In-m...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Henkel, Jörg Siddhu, Lokesh Nassar, Hassan Bauer, Lars Chen, Jian-Jia Hakert, Christian Seidl, Tristan Chen, Kuan Hsun Hu, Xiaobo Sharon Li, Mengyuan Yang, Chia-Lin Wei, Ming-Liang Karlsruhe Institute of Technology Germany TU Dortmund Germany University of Twente Netherlands University of Notre Dame IN United States National Taiwan University Taiwan

ISBN: (纸本)9798400710773

With the rapid development of the Internet of Things, machine learning applications on edge devices with limited resources face challenges due to large data scales and irregular memory access patterns. Non-volatile memory (NVM) technologies provide promising solutions by offering larger capacity, low leakage power, and data persistence. In this paper, we discuss the potential of NVM technology in enhancing machine learning applications by improving energy efficiency and reducing latency through in-memory computation and different NVM write modes. The insights from this analysis provide valuable guidance to device researchers and system architects working to develop high-performance systems for machine learning and accelerators in large-scale search applications using NVMs. © 2024 Copyright is held by the owner/author(s).

关键词： Computer aided instruction

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Technology for embedded GPU virtualization in the edge computing environment 19

Technology for embedded GPU virtualization in the edge compu...

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2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Yang, Xinyu Wang, Xin Yan, Lei Cao, Suzhi University of Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Key Laboratory of Space Utilization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350346558

With the rapid development of Internet of Things technology, the requirements for edge node data processing capability are increasing, and GPU processors are becoming more widely applied in edge nodes. Current research on GPU virtualization technology mainly focuses on cloud data centers, with little research on embedded GPU virtualization in scenarios with limited edge node resources. In contrast to cloud data centers, embedded GPUs in edge nodes typically do not have access to GPU utilization and video memory usage within each container. As a result, traditional GPU virtualization technologies are ineffective for resource virtualization on embedded devices. This paper presents a method to virtualize embedded GPU resources in an edge computing environment, called sGPU. We integrated edge nodes with embedded GPUs into Kubernetes via the device-plugin mechanism. Users can package GPU applications in containers and deploy them using Kubernetes on edge nodes with embedded GPUs. sGPU allows containers to share embedded GPU computing resources and divides a physical GPU into multiple virtual GPUs that can be allocated to containers on demand. To achieve GPU computing power division, we proposed a multi-container sharing GPU algorithm and implemented it in sGPU, which ensures the most accurate computing power segmentation under the competition of computing resources of a GPU used by multiple containers at the same time. According to the experimental results, the average overhead of sGPU is 3.28%. The accuracy of computing power segmentation is 92.7% when a single container uses GPU. © 2022 ieee.

关键词： Virtualization

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ALBADross: Active Learning Based Anomaly Diagnosis for Production HPC systems

ALBADross: Active Learning Based Anomaly Diagnosis for Produ...

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ieee international conference on Cluster computing (CLUSTER)

作者： Aksar, Burak Sencan, Efe Schwaller, Benjamin Aaziz, Omar Leung, Vitus J. Brandt, Jim Kulis, Brian Coskun, Ayse K. Boston Univ Elect & Comp Engn Boston MA 02215 USA Sandia Natl Labs Albuquerque NM 87123 USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Diagnosing causes of performance variations in High-Performance computing (HPC) systems is a daunting challenge due to the systems' scale and complexity. Variations in application performance result in premature job termination, lower energy efficiency, or wasted computing resources. One potential solution is manual root-cause analysis based on system telemetry data. However, this approach has become an increasingly time-consuming procedure as the process relies on human expertise and the size of telemetry data is voluminous. Recent research employs supervised machine learning (ML) models to diagnose previously encountered performance anomalies in compute nodes automatically. However, these models generally necessitate vast amounts of labeled samples that represent anomalous and healthy states of an application during training. The demand for labeled samples is constraining because gathering labeled samples is difficult and costly, especially considering anomalies that occur infrequently. This paper proposes a novel active learning-based framework that diagnoses previously encountered performance anomalies in HPC systems using significantly fewer labeled samples compared to state-of-the-art ML-based frameworks. Our framework combines an active learning-based query strategy and a supervised classifier to minimize the number of labeled samples required to achieve a target performance score. We evaluate our framework on a production HPC system and a testbed HPC cluster using real and proxy applications. We show that our framework, ALBADross, achieves a 0.95 F1-score using 28x fewer labeled samples compared to a supervised approach with equal F1-score, even when there are previously unseen applications and application inputs in the test dataset.

关键词： anomaly diagnosis active learning machine learning high performance computing

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Late Breaking Results: TriSC: Low-Cost Design of Trigonometric Functions with Quasi Stochastic computing 24

Late Breaking Results: TriSC: Low-Cost Design of Trigonometr...

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61st ACM/ieee Design Automation conference, DAC 2024

作者： Aygun, Sercan Shoushtari Moghadam, Mehran Najafi, M. Hassan University of Louisiana at Lafayette LafayetteLA United States

ISBN: (纸本)9798400706011

Low-cost and hardware-efficient design of trigonometric functions is challenging. Stochastic computing (SC), an emerging computing model processing random bit-streams, offers promising solutions for this problem. The existing implementations, however, often overlook the importance of the data converters necessary to generate the needed bit-streams. While recent advancements in SC bit-stream generators focus on basic arithmetic operations such as multiplication and addition, energy-efficient SC design of non-linear functions demands attention to both the computation circuit and the bit-stream generator. This work introduces TriSC, a novel approach for SC-based design of trigonometric functions enjoying state-of-the-art (SOTA) quasi-random bit-streams. Unlike SOTA SC designs of trigonometric functions that heavily rely on delay elements to decorrelate bit-streams, our approach avoids delay elements while improving the accuracy of the results. TriSC yields significant energy savings of up to 92% compared to SOTA. As two novel use cases studied for the first time in SC literature, we employ the proposed design for 2D image transformation and forward kinematics of a robotic arm, two computation-intensive applications demanding low-cost trigonometric designs. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Stochastic systems

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A Human Activity Recognition System Using Temporal Convolutional Network with Multi-Scale Temporal Attention Mechanism 4

A Human Activity Recognition System Using Temporal Convoluti...

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4th ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Sundari, V Soma Hossain, Ramy Read Sindhu, Monika Jansirani, D. Saranya, N. Naga Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq University Centre for Research & Development Chandigarh University Mohali India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India Saveetha Liberal Arts and Sciences Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

In recent years, the common adoption of smart glasses has created new opportunities for human and computer interaction mainly in Human Activity Recognition (HAR) domain. The smart glasses can capture a collection of wearer's activities data with the help of built-in sensors and cameras. However, the development of HAR systems for smart glasses led to significant challenges such as the requirement of real-time processing and effective computation on resource-constrained devices. Therefore, this research aims to develop an embedded Artificial Intelligence (AI) powered HAR system for smart glasses using Temporal Convolutional Network (TCN) with Multi-Scale Temporal Attention Mechanism (MSTAM). Initially, the data is gathered from HAR dataset, a complete source of human activities. Then, the collected data is further preprocessed with the help of Dynamic time Warping (DTW), as it aligns time-series signals of verifying lengths. After that, Haar Wavelet and Symlet Wavelet Coefficient Scattering (HW-SWCS) is employed to extract the features from preprocessed signals. Finally, TCN-MSTAM is introduced to recognize and classify the human activities. From the results, the proposed TCN-MSTAM model offered outstanding results in accuracy of 93.4% when compared with existing Sensors based on a Capsule Network (SensCapsNet). © 2024 ieee.

关键词： Metadata

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Machine Learning-Based Driving Style Classification real-World Data Prediction 4

Machine Learning-Based Driving Style Classification Real-Wor...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Lee, Gye-Seong Gwangju Korea Republic of

ISBN: (纸本)9798331531225

Driving style remains a critical factor that influences traffic behavior and, hence, risks of accidents even when Advanced Driver Assistance systems (ADAS) and autonomous driving technologies are increasingly available. In this study, a machine learning-based methodology is proposed for classifying driving into three categories: conservative, normal, and aggressive. Simulation data were used for training the model, although real-world data applications were given for a rigorous evaluation to ensure practical applicability. Several key features, such as acceleration, deceleration, lateral acceleration, throttle input, and brake usage, were analyzed for the classification. The Random Forest model, through comparative analysis, was selected based on better accuracy and generalization. A sliding window technique and statistical feature extraction were used to improve dataset diversity and learning on time-series data. It worked with an 86.7% accuracy rate on real-world data, which sheds light on its robustness for boosting driver behavior analysis as well as personalized driver-assistance technologies. © 2024 ieee.

关键词： Advanced driver assistance systems

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Internet of Things (IoT)-Based Smart Blood Bank Services: A Review 15

Internet of Things (IoT)-Based Smart Blood Bank Services: A ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Babu, J. Chinna Khan, Mudassir Nandini, K. Reddysekhar, E. Kumar, G. Pavan Sathwika, B. Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Andhra Pradesh Rajampet India King Khalid University College of Science & Arts Tanumah Department of Computer Science Abha Saudi Arabia

ISBN: (纸本)9798350370249

This review includes various methods or concepts that have been proposed by various authors for enhancing the offerings of blood banks. Smart blood bank services have been the subject of a great deal of research. Authors from several fields have provided their suggested models. An automated blood bank that uses SMS was created by Dr. G. F. Ali Ahmed, Pavitra H. V., utilizing an embedded technology. By employing mobile applications and IoT cloud services, Saravanan Subbiah created an integrated circuit. Vandana Khare IOT-based blood bank services were provided by T. Sravani. Based on the suggested system for blood bank services, Fizakhan. On blood bank services, S.S. Thorat made a proposal. © 2024 ieee.

关键词： Blood bank IOT Smart systems

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M-DRTA: A Distributed Runtime Monitoring and Assurance Framework for Multi-vehicle Behavior Planning

M-DRTA: A Distributed Runtime Monitoring and Assurance Frame...

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Yanfei Peng Guozhen Tan Xiang Wang School of Computer Science and Technology Dalian University of Technology Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

The machine learning-based decision algorithms commonly used in autonomous driving have led to the Safety of the Intended Functionality(SOTIF) issues due to their potential lack of functionality. To address these limitations, we propose M-DRTA, a distributed runtime assurance framework based on machine learning, which can provide safety assurance for multi-autonomous vehicles by making functional improvements to narrow the vehicle safety zone. We secure the entire multi-vehicle driving system by maintaining safety in local vehicles. In the M-DRTA, an independent runtime assurance framework is provided for each autonomous vehicle through redundant functional modules that include a deep neural network-based advanced controller, a recoverable safety controller, and a monitoring and assurance module. Monitor SOTIF risks by identifying trigger conditions, under-function status, etc., and provide safety through permission handover without unduly sacrificing performance. We tested and evaluated M-DRTA for the different number of vehicle states in a driving task. The experimental results show that M-DRTA can strike a proper balance between safety and efficiency compared to the baseline approach.

关键词： Runtime Machine learning algorithms Computer architecture Handover Deep reinforcement learning real-time systems Safety Planning Monitoring Autonomous vehicles

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An embedded System based Curtain Controlling Unit using NRF Module 5

An Embedded System based Curtain Controlling Unit using NRF ...

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Sidral, Akshay Thakare, Vaishnav Sontakke, Rohini Gaikwad, Dinesh Rajarapollu, Prachi MIT Academy of Engineering Alandi School of Electrical Engineering Pune India

ISBN: (纸本)9798350367171

With the growing interconnectedness and intelligent automation, the 21st century is seeing a fast change in technology, industries, and practices. The concept of 'interconnectivity' first appeared in the context of intelligent control systems, which promoted interactions and communication between technology, sensors, and people. Numerous real-world applications for these systems exist in the industrial sector, home automation, online banking, and other industries. This study proposes a novel embedded system based curtain controlling unit using NRF module in response to the quickly expanding automation. The proposed system uses a NRF24L01 transceiver and an Atmega328p microcontroller to communicate via GPSK modulation. By pressing the switch on the rotary encoder, the user may control the motors (which are linked to the curtains). Above the OLED display, the curtain status is displayed. Given that the system employs a small chip CPU and a NRF24L01 transceiver using GPSK modulation for transmission, it is most likely designed for enabling low-power and wireless communication. This might be helpful in situations where running wires is difficult or impracticable in the areas like Auditoriums, Hospitals and old age homes, where mobility of humans remain as a major challenge. © 2024 ieee.

关键词： Microcontrollers

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A Demonstration Platform for Large-Scaled Point Cloud Network Based on 28nm 2D/3D Unified Sparse Convolution Accelerator 5

A Demonstration Platform for Large-Scaled Point Cloud Networ...

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5th ieee international conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Feng, Xiaoyu Sun, Wenyu Fan, Shupei Tang, Chen Yang, Yixiong Yue, Jinshan Liao, Qingmin Yang, Huazhong Liu, Yongpan Tsinghua University China Tsinghua Shenzhen International Graduated School China Chinese Academy of Sciences China

ISBN: (纸本)9798350332674

3D point cloud processing plays an important role in many emerging applications such as autonomous driving, visual navigation, and virtual reality. It calls for hardware acceleration of multiple key operations, including 3D Submanifold SCONV, 3D non-Submanifold SCONV, and 2D SCONV. This work presents a 2D/3D unified sparse convolution accelerator for large-scale voxel-based point cloud networks. The chip is fabricated in TSMC 28nm CMOS technology to achieve 3.3-16.9 FPS running from 60-400MHz when computing the SECOND network on KITTI dataset. This work has been included by ISSCC2023 [1]. A demonstration is given to show the real-time 3D processing with a lidar sensor. © 2023 ieee.

关键词： Convolution

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