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A Digital SRAM computing-in-Memory Design Utilizing Activation Unstructured Sparsity for high-Efficient DNN Inference 26

A Digital SRAM Computing-in-Memory Design Utilizing Activati...

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26th IEEE-computer-Society Annual symposium on VLSI (ISVLSI)

作者： Zhong, Baiqing Wang, Mingyu Zhang, Chuanghao Mai, Yangzhan Li, Xiaojie Yu, Zhiyi Sun Yat Sen Univ Sch Microelect Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9798350327694

the computing-in-Memory (CIM) architecture has emerged as a promising approach for designing energy-efficient DNN processors. While previous CIM designs have explored the use of DNN weight sparsity, these approaches often involve pruning the weight matrix in a specific manner. this process may increase the new complexity of the calculation and negatively impact DNN accuracy. However, there are barely any digital CIM circuits that leverage the sparsity in activation which is naturally sparse in many scenarios due to the ReLU activation functions. In order to fully utilize activation unstructured sparsity, we proposed a digital SRAM CIM. this circuit is designed using the booth encoding scheme and adopts the circuit structure of an accumulator-based multiply-accumulate (MAC) calculation. It utilizes SRAM bit-line (BL) computing to obtain matrix sparse information and employs an allocator to allocate data calculation for SRAM-CIM. the proposed design is implemented and evaluated at 40 nm CMOS process. Our evaluation results show that the proposed circuit can achieve a clock frequency of 1 GHz at 1.1 V, with a peak performance of 819.2 GOPS, and in the case of 50%-90% sparsity, SRAM-CIM achieves 1.12 times 3.32 times speedup, and energy savings of 48.2% to 90.57% over dense mode. When performing an 8-bit matrix multiplication with 90% sparsity, the energy efficiency is 10.57 TOPS/W. © 2023 IEEE.

关键词： Compute-In-Memory SRAM Sparsity Booth-encode DNN

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JobViewer: Graph-based Visualization for Monitoring high-performance computing System 9

JobViewer: Graph-based Visualization for Monitoring High-Per...

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IEEE/ACM 15th International Conference on Utility and Cloud computing (UCC) / 9th International Conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Dang, Tommy Nguyen, Ngan V. T. Li, Jie Sill, Alan Hass, Jon Chen, Yong Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Texas Tech Univ Ctr High Performance Comp Lubbock TX 79409 USA Dell Inc Austin TX USA

ISBN: (纸本)9781665460903

Visualization aims to strengthen data exploration and analysis, especially for complex and high-dimensional data. high-performance computing (HPC) systems are typically large and complicated instruments that generate massive performance and operation time series. Monitoring HPC systems' performance is a daunting task for HPC admins and researchers due to their dynamic natures. this work proposes a visual design using the bi-partite graph's idea to visualize HPC clusters' structure, metrics, and job scheduling data. We built a web-based prototype, called JobViewer, that integrates advanced methods in visualization and human-computer interaction (HCI) to demonstrate the benefits of visualization in real-time monitoring HPC centers. We also showed real use cases and a user study to validate the efficiency and highlight the current approach's drawbacks.

关键词： high-performance computing Center Job Scheduling Radar Charts Time-Series Data Analysis Multidimensional Data Visualization

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An Intelligent Routing Mechanism Based on Deep Reinforcement Learning 15

An Intelligent Routing Mechanism Based on Deep Reinforcement...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Wu, Shangyang Yi, Bo Song, Yang Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350329360

With the continuous development of network technologies such as cloud computing and mobile computing, the demand of services is increasing significantly, which brings exponential grow of network traffic. In order to solve the routing problem of high concurrent traffic on the premise of ensuring QoS, an intelligent routing optimization mechanism is needed to realize the traffic efficient forwarding. Software-defined networks(SDN) provide the basis for programmable routing. Additionally, SDN and IPv6 are compatible with natural programmable capabilities, and technologies such as BIERv6 and iFIT can be deployed on it. In SDN, the controller can control the traffic routing by modifying the routing table in the switch, but the inefficient routing decision will lead to the overload of the network link and increase the end-to-end transmission delay. Deep reinforcement learning has the ability of intelligent decision-making and processing large-scale high-dimensional data, which is suitable for solving decision optimization problems such as network routing decision-making. In this paper, we proposed an intelligent routing optimization model based on SDN. the model uses deep reinforcement learning to learn the relationship between the traffic load of switches and network performance, and uses Nadam optimizer to update gradients to determine the link weights in the network. SDN controller determines the routing forwarding path through the link weights. © 2023 IEEE.

关键词： Reinforcement learning

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Renting Edge computing Resources for Service Hosting 15th

Renting Edge Computing Resources for Service Hosting

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15th International Conference on performance Evaluation Methodologies and Tools, VALUETOOLS 2022

作者： Madnaik, Aadesh Moharir, Sharayu Karamchandani, Nikhil Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9783031312335

We consider the setting where a service is hosted on a third-party edge server deployed close to the users and a cloud server at a greater distance from the users. Due to the proximity of the edge servers to the users, requests can be served at the edge with low latency. However, as the computation resources at the edge are limited, some requests must be routed to the cloud for service and incur high latency. the system’s overall performance depends on the rent cost incurred to use the edge server, the latency experienced by the users, and the cost incurred to change the amount of edge computation resources rented over time. the algorithmic challenge is to determine the amount of edge computation power to rent over time. We propose a deterministic online policy and characterize its performance for adversarial and stochastic i.i.d. request arrival processes. We also characterize a fundamental bound on the performance of any deterministic online policy. Further, we compare the performance of our policy with suitably modified versions of existing policies to conclude that our policy is robust to temporal changes in the intensity of request arrivals. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Edge computing

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DigiQ: A Scalable Digital Controller for Quantum computers Using SFQ Logic 28

DigiQ: A Scalable Digital Controller for Quantum Computers U...

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28th Annual IEEE International symposium on high-performance computer architecture (HPCA)

作者： Jokar, Mohammad Reza Rines, Richard Pasandi, Ghasem Cong, Haolin Holmes, Adam Shi, Yunong Pedram, Massoud Chong, Frederic T. Univ Chicago Dept Comp Sci Chicago IL 60637 USA Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA Super Tech Chicago IL USA NVIDIA Santa Clara CA USA Amazon Braket Seattle WA USA HRL Labs LLC Malibu CA USA

ISBN: (纸本)9781665420273

the control of cryogenic qubits in today's superconducting quantum computer prototypes presents significant scalability challenges due to the massive costs of generating/routing the analog control signals that need to be sent from a classical controller at room temperature to the quantum chip inside the dilution refrigerator. thus, researchers in industry and academia have focused on designing in-fridge classical controllers in order to mitigate these challenges. Due to the maturity of CMOS logic, many industrial efforts (Microsoft, Intel) have focused on Cryo-CMOS as a near-term solution to design in-fridge classical controllers. Meanwhile, Superconducting Single Flux Quantum (SFQ) is an alternative, less mature classical logic family proposed for large-scale in-fridge controllers. SFQ logic has the potential to maximize scalability thanks to its ultra-high speed and very low power consumption. However, architecture design for SFQ logic poses challenges due to its unconventional pulse-driven nature and lack of dense memory and logic. thus, research at the architecture level is essential to guide architects to design SFQ-based classical controllers for large-scale quantum machines. In this paper, we present DigiQ, the first system-level design of a Noisy Intermediate Scale Quantum (NISQ)-friendly SFQ-based classical controller. We perform a design space exploration of SFQ-based controllers and co-design the quantum gate decompositions and SFQ-based implementation of those decompositions to find an optimal SFQ-friendly design point that trades area and power for latency and control while ensuring good quantum algorithmic performance. Our co-design results in a single instruction, multiple data (SIMD) controller architecture, which has high scalability, but imposes new challenges on the calibration of control pulses. We present software-level solutions to address these challenges, which if unaddressed would degrade quantum circuit fidelity given the imperfections of q

关键词： SFQ-based quantum gate Quantum optimal control Scalable quantum computer Cryogenic electronic

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A Nano-based high-Speed QCA circuit for Information Security with Image Masking 15

A Nano-based High-Speed QCA circuit for Information Security...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Seyedi, Saeid Abdoli, Hatam Department of Computer Engineering Faculty of Engineering Bu-Ali Sina University Hamedan Iran

ISBN: (纸本)9798331522254

A potential method for nanoscale computing, quantum-dot cellular automata (QCA) makes use of the electron tunneling between quantum dots inside a cell as well as the electrostatic interaction between surrounding dots. QCA technology outperforms traditional complementary metal-oxide semiconductor (CMOS) technology in terms of speed, power consumption, security, and space utilization. Due to the importance of security, this article investigates utilizing QCA to generate picture masks and security in these pictures. the mask picture is created by an encoder circuit, which may also be used as a decoder. Even if the secret key is still unknown, the original picture can be retrieved using the mask image. Improving the security characteristics in nano-communication, which uses QCA cells and nanodevices, is essential. Image masking entails encrypting an image using a random key to generate an encrypted picture, also known as a cipher image. After that, the original picture and the cipher image are merged to create a mask image, a hidden information set for users. With the secret key, this mask picture may obtain an image from another cipher image created with the same set of random keys. To ensure security in this paper, the suggested QCA encoder generates the mask picture. the efficacy of these designs was confirmed by using QCADesigner for functional verification. the simulation results show that the suggested solutions not only work well but also much surpass earlier approaches in terms of speed and space consumption. the higher performance of these optimum designs over other circuit designs highlights the potential of QCA technology to improve picture masking and security. © 2024 IEEE.

关键词： Semiconductor quantum dots

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A Parallel Slime Mould Algorithm with Boundary Rebound and Knowledge-Sharing Mechanism 15th

A Parallel Slime Mould Algorithm with Boundary Rebound and...

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15th International Conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Fan, HongYe Chu, Shu-Chuan Sui, Xiao Pan, Jeng-Shyang School of Computer Science Northeast Electric Power University Jilin Jilin City132012 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

the conventional slime mould method has high global search ability but has certain drawbacks such as sluggish convergence speed, limited accuracy, and the tendency to slip into a locally optimum solution. Aiming at the algorithm’s shortcomings, we propose an improved stochastic optimization algorithm—a parallel Slime Mould Algorithm with Boundary rebound and Knowledge-sharing mechanism (PKB-SMA). the main improvement measures are as follows: (1) Introduce a parallel technique to balance the algorithm’s capacity for exploration and development. (2) Propose a knowledge-sharing system to improve individual quality. (3) Using boundary rebound method to improve individual exploration ability. (4) the population was initialized by Hammersley sampling method. Several test functions are utilized to validate the performance of the PKB-SMA method, and its experimental findings are compared to those of competing algorithms. © 2024, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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TianMen: a DPU-based storage network offloading structure for disaggregated datacenters 15

TianMen: a DPU-based storage network offloading structure fo...

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15th Annual ACM symposium on Cloud computing, SoCC 2024

作者： Zhao, Weiyue Wu, Jingya Lu, Wenyan Li, Xiaowei Yan, Guihai State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Yusur Technology Co. Ltd. Beijing China

ISBN: (纸本)9798400712869

In modern disaggregated datacenters, the storage network which interconnects the compute and memory pools becomes the performance bottleneck. the high-end RDMA devices cannot meet the complex requirements of storage networks, due to the limited RDMA semantics and throughput. Existing solutions essentially follow the monolithic design, so they suffer from underutilized resources and high scaling costs. In this paper, we design TianMen, which offloads the storage network by extending RDMA semantics and customizing communication hardware structure. Specifically, we use DPU as the infrastructure, leveraging the rich storage and compute resources. TianMen enables fully disaggregated storage system that bypasses the server-side CPU, and supports elastic resource pools. Experimental results show that, compared with state-of-the-art solutions: 1) Tian-Men achieves 1 RTT for GET/PUT operations and up to 6× access acceleration;2) TianMen provides CPU bypass storage network management, including 3.2× speedup of metadata consistency management, per-request load balancing, and 10s microsecond-level fault recovery latency;3) TianMen saturates the communication bandwidth when processing small payload, increasing the bandwidth utilization by 34.2%. And TianMen achieves 2.27× throughput compared to the commercial RNICs. © 2024 Owner/Author.

关键词： Storage management

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ExtendLife: Weights Mapping Framework to Improve RRAM Lifetime for Accelerating CNN 15th

ExtendLife: Weights Mapping Framework to Improve RRAM Lifeti...

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15th International symposium on Advanced Parallel Processing Technologies, APPT 2023

作者： Yang, Fan Li, Yusen Niu, Zeyuan Wang, Gang Liu, Xiaoguang College of Computer Science Nankai University Tianjin China College of Information Engineering Southeast University Nanjing China

ISBN: (纸本)9789819978717

Process-in-memory (PIM) engines based on Resistive random-access memory (RRAM) are used to accelerate the convolutional neural network (CNN). RRAM performs computation by mapping weights on its crossbars and applying a high voltage to get results. the computing process degrades RRAM from the fresh status where RRAM can support high data precision to the aged status where RRAM only can support low precision, potentially leading to a significant CNN training accuracy degradation. Fortunately, many previous studies show that the impact of loss caused by the RRAM precision limitation across various weights is different for CNN training accuracy, which motivates us to consider mapping different weights on RRAM with different statuses to keep high CNN training accuracy and extending the high CNN training accuracy iterations of PIM engines based on RRAM, which is regarded as the lifetime of the RRAM on CNN training. In this paper, we propose a method to evaluate the performance of the weights mapping on extending the lifetime of the RRAM and present a weights mapping framework specifically designed for the hybrid of aged and fresh RRAM to extend the lifetime of the RRAM engines on CNN training. Experimental results demonstrate that our weights mapping framework brings up to 6.3× on average lifetime enhancement compared to the random weights mapping. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mapping

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Enhancing Machine Learning performance in Dynamic Cloud Environments with Auto-Adaptive Models 15

Enhancing Machine Learning Performance in Dynamic Cloud Envi...

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15th IEEE International Conference on Cloud computing Technology and Science, CloudCom 2024

作者： Nguyen, Chanh Bhuyan, Monowar Elmroth, Erik Umeå University Department of Computing Science UmeåSE-90187 Sweden

ISBN: (纸本)9798331507589

Autonomous resource management is essential for large-scale cloud data centers, where Machine Learning (ML) enables intelligent decision-making. However, shifts in data patterns within operational streams pose significant challenges to sustaining model accuracy and system efficiency. this paper proposes an auto-adaptive ML approach to miti-gate the impact of data drift in cloud systems. A knowledge base of distinct time-series batches and corresponding ML models is constructed and clustered using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. When model performance degrades, the system uses Dynamic Time Warping (DTW) to retrieve matching hyper-parameters from the knowledge base and apply them to the deployed model, optimizing inference accuracy on new data streams. Experiments with two real-world cloud data traces - representing both stable and highly fluctuating environments - demonstrate that the proposed approach maintains high model accuracy (over 89%) while minimizing retraining costs. Specifically, for the Wikipedia trace with frequent data drift, retraining overhead is reduced by 22.9% compared to drift detection-based retraining and by 97% compared to incremental retraining. In stable environments, like the Google cluster trace, retraining costs decrease by 96.3% and 88.9%, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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