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Ascend: a Scalable and Unified architecture for Ubiquitous Deep Neural Network computing 27

Ascend: a Scalable and Unified Architecture for Ubiquitous D...

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27th IEEE International symposium on high-performance computer architecture (HPCA)

作者： Liao, Heng Tu, Jiajin Xia, Jing Liu, Hu Zhou, Xiping Yuan, Honghui Hu, Yuxing Huawei Shenzhen Peoples R China

ISBN: (纸本)9781665422352

Deep neural networks (DNNs) have been successfully applied to a great variety of applications, ranging from small IoT devices to large scale services in a data center. In order to improve the efficiency of processing these DNN models, dedicated hardware accelerators are required for all these scenarios. theoretically, there exists an optimized acceleration architecture for each application. However, considering the cost of chip design and corresponding tool-chain development, researchers need to trade off between efficiency and generality. In this work, we demonstrate that it is practical to use a unified architecture, called Ascend, to support those applications, ranging from IoT devices to data-center services. We provide a lot of design details to explain that the success of Ascend relies on contributions from different levels. First, heterogeneous computing units are employed to support various DNN models. And the datapath is adapted according to the requirement of computing and data access. Second, when scaling the Ascend architecture from a single core to a cluster containing thousands of cores, it involves design efforts, such as memory hierarchy and system level integration. third, a multi-tier compiler, which provides flexible choices for developers, is the last critical piece. Experimental results show that using accelerators based on the Ascend architecture can achieve comparable or even better performance in different applications. In addition, various chips based on the Ascend architecture have been successfully commercialized. More than 100 million chips have been used in real products.

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QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits 28

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuit...

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

作者： Wang, Hanrui Ding, Yongshan Gu, Jiaqi Lin, Yujun Pan, David Z. Chong, Frederic T. Han, Song MIT Cambridge MA 02139 USA Yale Univ New Haven CT 06520 USA Univ Texas Austin Austin TX 78712 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781665420273

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research has explored a higher level of optimization by making the quantum circuits themselves resilient to noise. In this paper, we propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping. Variational quantum circuits are a promising approach for constructing quantum neural networks for machine learning and variational ansatzes for quantum simulation. However, finding the best variational circuit and its optimal parameters is challenging due to the large design space and parameter training cost. We propose to decouple the circuit search from parameter training by introducing a novel SuperCircuit. the SuperCircuit is constructed with multiple layers of pre-defined parameterized gates (e.g., U3 and CU3) and trained by iteratively sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuits performance trained from scratch. then we perform an evolutionary co-search of SubCircuit and its qubit mapping. the SubCircuit performance is estimated with parameters inherited from SuperCircuit and simulated with real device noise models. Finally, we perform iterative gate pruning and finetuning to remove redundant gates in a fine-grained manner. Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 14 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noise-adaptive qubit mapping baselines. For QML tasks, QuantumNAS is the first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class classification accuracy on real quantum computers. It also achieves the lowest eigenvalue for VQE tasks on H-2, H2O, LiH, CH4, BeH2 compared w

关键词： Quantum computing Quantum Noise Variational Quantum Algorithm Quantum Machine Learning Neural Networks Qubit Mapping VQE QNN

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Exploring the Efficiency of Renewable Energy-based Modular Data Centers at Scale 15

Exploring the Efficiency of Renewable Energy-based Modular D...

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15th Annual ACM symposium on Cloud computing, SoCC 2024

作者： Sun, Jinghan Gong, Zibo Agarwal, Anup Noghabi, Shadi Chandra, Ranveer Snir, Marc Huang, Jian Uiuc United States Cmu United States Microsoft Research United States

ISBN: (纸本)9798400712869

Modular data centers (MDCs) that can be placed right at the energy farms and powered mostly by renewable energy, is a flexible and effective approach to lowering the carbon footprint of data centers. However, the main challenge of using renewable energy is the high variability of power produced, which implies large volatility in powering computing resources at MDCs, and degraded application performance due to the task evictions and migrations. this causes challenges for platform operators to decide the MDC deployment. To this end, we present SkyBox, a framework that employs a learning-based approach for platform operators to explore the efficient use of renewable energy with MDC deployment across geographical regions. SkyBox is driven by the insights based on our study of real-world power traces from a variety of renewable energy farms - the predictable production of renewable energy and the complementary nature of energy production patterns across different renewable energy sources and locations. With these insights, SkyBox uses the coefficient of variation metric to select the qualified renewable farms, it can identify a set of farms with complementary energy production patterns with a subgraph identification algorithm. After that, SkyBox enables smart workload placement and migrations to further tolerate the power variability. Our experiments with real power traces and datacenter workloads show that SkyBox has the lowest carbon emissions compared with existing approaches. SkyBox also minimizes the negative impact of the power variability on cloud applications, enabling it an effective solution of utilizing renewable energy for modern data centers. © 2024 Owner/Author.

关键词： cloud computing modular data center Renewable energy workload scheduling

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GEYSER: A Compilation Framework for Quantum computing with Neutral Atoms 22

GEYSER: A Compilation Framework for Quantum Computing with N...

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49th IEEE/ACM Annual International symposium on computer architecture (ISCA)

作者： Patel, Tirthak Silver, Daniel Tiwari, Devesh Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450386104

Compared to widely-used superconducting qubits, neutral-atom quantum computing technology promises potentially better scalability and flexible arrangement of qubits to allow higher operation parallelism and more relaxed cooling requirements. the high performance computing (HPC) and architecture community is beginning to design new solutions to take advantage of neutral-atom quantum architectures and overcome its unique challenges. We propose GEYSER, the first work to take advantage of the multi-qubit gates natively supported by neutral-atom quantum computers by appropriately mapping quantum circuits to three-qubit-friendly physical arrangement of qubits. then, GEYSER creates multiple logical blocks in the quantum circuit to exploit quantum parallelism and reduce the number of pulses needed to realize physical gates. these circuit blocks elegantly enable GEYSER to compose equivalent circuits with three-qubit gates, even when the original program does not have any multi-qubit gates. Our evaluation results show GEYSER reduces the number of operation pulses by 25%-90% and improves the algorithm's output fidelity by 25%-60% points across different algorithms.

关键词： Quantum computing NISQ computing Neutral Atoms Rydberg Atoms Quantum Compiling Quantum Software

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Multi-Class Breast Cancer Classification from Digital Mammograms Using Vision Transformers 2023

Multi-Class Breast Cancer Classification from Digital Mammog...

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15th International Conference on Contemporary computing, IC3 2023

作者： Salgarkar, Varun Sunil Ilavarasi, A.K. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology Chennai India

ISBN: (纸本)9798400700224

Breast cancer stands as a prevalent form of neoplastic disease afflicting women worldwide, with timely detection playing a pivotal role in ensuring favorable treatment outcomes. In this paper, we propose a methodology for multi-class breast cancer classification using Vision Transformers, a powerful architecture for learning long-range dependencies. CNNs utilizing deep backbones have demonstrated remarkable performance in medical image analysis tasks, including disease classification, tumor segmentation, and lesion detection. Nevertheless, CNNs' inductive bias towards locality limits their ability to extract global feature information from breast cancer mammogram images, resulting in suboptimal classification results. To overcome this shortcoming, Vision Transformer (ViT) is a promising deep learning model for computer vision tasks, but its applicability to medical image classification is limited by the need for large-scale datasets and high computational power. Our results show that Vision Transformers have the potential to outperform convolutional neural networks in image-based computer-aided diagnosis (CAD) schemes. DeiT is more efficient for small-scale datasets, as it can effectively learn general features through self-supervised pre-training. On the other hand, Swin Transformer's hierarchical structure and linear computational complexity make it a computationally effective alternative. We applied the proposed methodology on two variants of two different Vision Transformer architectures, Swin and DeiT, and achieved high accuracy and AUC values for differentiating normal, benign, and malignant cases. the models are trained on Inbreast dataset obtained from Kaggle after which they are evaluated on accuracy, precision, recall, the F1-score and training time. the DeiT-B transformer model beats the Swin-S on accuracy by 11.29% achieving a highest accuracy of 88.50% with ROC of 90, 92 and 96 for normal, benign and malignant classes respectively. © 2023 ACM.

关键词： Image classification

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Turn-aware Application Mapping using Reinforcement Learning in Power Gating-enabled Network on Chip 15

Turn-aware Application Mapping using Reinforcement Learning ...

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15th IEEE International symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Shammasi, Mohammadmehdi Baharloo, Mohammad Abdollahi, Meisam Baniasadi, Amirali University of Victoria Department of Electrical and Computer Engineering Victoria Canada

ISBN: (纸本)9781665464994

As the backbone for many-core chips, Network-on-chips (NoCs) consume a significant share of total chip power. As a result, decreasing the power consumption in these components can reduce the total chip's power significantly. NoC's routers can be powered down using power-gating, a promising technique for reducing static power consumption. In some advanced methods, routers are put in sleep mode and only wake up when they are needed to turn/inject packets. Since waking up the router takes several cycles to complete, packets will experience high latency. In this regard, application mapping significantly impacts the number of turns. this article proposes a reinforcement learning (RL) framework based on Actor-Critic architecture to optimize the application mapping problem to minimize the number of turn packets as well as communication cost. Our RL framework learns the heuristic of the mapping problem and outputs a near-optimal mapping. A 2-opt local search algorithm fine-tunes this strategy and provides an improved mapping. Our simulations show that the proposed RL framework can achieve better cost and algorithm run-time performance compared to other heuristic algorithms such as Simulated Annealing (SA) and Genetic Algorithm (GA). © 2022 IEEE.

关键词： Network-on-chip

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Interval Type-2 Fuzzy-Support Vector Regression in Representation of Uncertainty in a Non-linear System 15th

Interval Type-2 Fuzzy-Support Vector Regression in Represent...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Umoh, Uduak Eyoh, Imo Asuquo, Daniel Murugesan, Vadivel Alimot, Olanrewaju Department of Computer Science University of Uyo PMB 1017 Akwa Ibom Uyo Nigeria TEFTFund Centre of Excellence in Computational Intelligence Research University of Uyo Uyo Nigeria School of Management National Institute of Technology Karnataka Surathkal India VIT Business School Vellore Institute of Technology Vandalur - Kelambakkam Road Chennai6000127 India

ISBN: (纸本)9783031810824

Machine learning algorithms such as Support Vector Machine (SVM and Support Vector Regression (SVR) are faced with challenges when confronted with imprecise and noisy data, which can lead to less meaningful outcomes. this paper introduces Interval Type-2 Fuzzy Support Vector Regression (IT2FSVR) as a solution to address uncertainty in non-linear systems. By combining Interval Type-2 Fuzzy Sets (IT2FS) and SVR, the proposed method enhances performance in systems with high levels of noise and non-linearity. the integration of IT2F membership in SVR directly tackles uncertainty in prediction problems, enabling adaptive learning to varying inputs and improving generalization performance. To demonstrate the effectiveness of this approach, the authors tested the performance of IT2F-SVR using a dataset of cardiovascular disease patients. Experimental results demonstrate that IT2F-SVR effectively eliminates uncertainty and significantly improves the learning process, outperforming individual approaches when applied to the same dataset and achieving faster execution times compared to some alternatives, albeit taking more time than SVR. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Membership functions

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Service Function Chaining on SDN/NFV based programmable data plane

Service Function Chaining on SDN/NFV based programmable data...

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International Conference on computing and Networking Technology (ICCNT)

作者： Meignanamoorthi Dhanapani V. Vetriselvi Department of Computer Science and Engineering (DCSE) College of Engineering Guindy Anna university Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are transforming next-generation networks. SDN’s data plane programmability unlocks new levels of network flexibility. NFV simplifies management of essential network functions like firewalls and intrusion detection systems. Service Function Chaining (SFC) offers increased manageability by defining ordered sequences of these functions as virtual chains. However, enforcing SFC through traditional network switches is complex. this paper proposes a novel architecture that leverages the power of SDN and NFV for efficient SFC packet processing. By utilizing the programmability of modern switches, our design enforces SFC directly on the data plane. Experimental results demonstrate significant performance improvements in processing time compared to the widely used OpenVSwitch. this approach paves the way for high-performance and scalable SFC enforcement in next-generation networks.

关键词： Service function chaining Firewalls (computing) Intrusion detection computer architecture Network function virtualization Next generation networking

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Automatic Retinal Vessel Segmentation Using BTLBO 1

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11th International Conference on Soft computing for Problem Solving, SocProS 2022

作者： Rajesh, Chilukamari Kumar, Sushil Department of Computer Science and Engineering National Institute of Technology Warangal Telangana506004 India

ISBN: (数字)9789811965258

ISBN: (纸本)9789811965241

the accuracy of retinal vessel segmentation (RVS) is crucial in assisting physicians in the ophthalmology diagnosis or other systemic diseases. However, manual segmentation needs a high level of knowledge, time-consuming, complex, and prone to errors. As a result, automatic vessel segmentation is required, which might be a significant technological breakthrough in the medical field. We proposed a novel strategy in this paper, that uses neural architecture search (NAS) to optimize a U-net architecture using a binary teaching learning-based optimization (BTLBO) evolutionary algorithm for RVS to increase vessel segmentation performance and reduce the workload of manually developing deep networks with limited computing resources. We used a publicly available DRIVE dataset to examine the proposed approach and showed that the discovered model generated by the proposed approach outperforms existing methods. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ophthalmology

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Real-time Indian Sign Language Recognition using Skeletal Feature Maps 14

Real-time Indian Sign Language Recognition using Skeletal Fe...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Vishal Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Agarwal, Agrima Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350335095

Sign language recognition is a widely researched problem that has gained momentum since the advent of deep learning. Many researchers have designed models using different deep learning architectures claiming high validation accuracy but low real-time performance. the proposed model achieves consistent prediction in real time through Skeletal Feature Maps using keypoints extracted by MediaPipe. these images are passed to a novel 15-layer CNN architecture for classification. the proposed model can achieve a high validation accuracy of 99.84% over a self-created 106-word Indian Sign Language dataset, that is higher than other machine learning models such as SVM, Gaussian Naive Bias, Decision Tree, Random Forest and some pre-trained standard CNN architectures. the salient feature of the proposed model is it is consistent and accurate independent of the background, subject and lighting conditions. Further, the model is implemented as a desktop application and tested by different signers. © 2023 IEEE.

关键词： Decision trees

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