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RL-Driven Fine-Grained Power Gating for Modern Processors Using Multi-Layer Perceptron Models

RL-Driven Fine-Grained Power Gating for Modern Processors Us...

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IEEE International Symposium on Quality Electronic Design

作者： Naman Kalra Jaynarayan T Tudu Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Indian Institute of Technology Tirupati India

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

This paper introduces an RL-driven algorithm for fine-grained power gating of functional units in superscalar processors. The proposed algorithm leverages a Multi-Layer Perceptron (MLP) neural network as a policy model, trained by a reinforcement learning (RL) agent, to optimize power states across four modes: On, Sleep-on, Sleep-off, and Off. By statically analyzing features extracted from the control flow graph (CFG) and execution profile of the program, the RL-driven model selects the most energy-efficient power states for each functional unit without compromising performance. Traditional power gating techniques often struggle with tradeoffs between minimizing performance loss (wake-up latency) and reducing power consumption, necessitating a reevaluation of power management strategies. Next-generation complex SoC designs at < 7nm technology nodes require a finer granularity approach to effectively manage power states. Our AI-based algorithm provides an optimized trade-off between power efficiency and performance by enabling power gating across multiple power states and selecting the power state that has the effectively least energy-delay product. Additionally, we propose an architectural circuit design for power gating multiple FU's within a superscalar processor, enabling efficient state transitions. Preliminary experimental results show that the proposed AI-based method achieves 10-20% energy savings while improving performance by 1.5% compared to traditional power gating algorithms.

关键词： Program processors Power demand Power system management Reinforcement learning Performance gain Feature extraction Energy efficiency Circuit synthesis Flow graphs Next generation networking

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Pruning Binarized Neural Networks Enables Low-Latency, Low-Power FPGA-Based Handwritten Digit Classification

Pruning Binarized Neural Networks Enables Low-Latency, Low-P...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Syamantak Payra Gabriel Loke Yoel Fink Joseph D. Steinmeyer Dept. of Electrical Engineering Stanford University (formerly Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology) Dept. of Materials Science and Engineering Massachusetts Institute of Technology Dept. of Materials Science and Engineering Dept. of Electrical Engineering and Computer Science and Institute for Soldier Nanotechnologies Massachusetts Institute of Technology Dept. of Electrical Enigneering and Computer Science Massachusetts Institute of Technology

As neural networks are increasingly deployed on mobile and distributed computing platforms, there is a need to lower latency and increase computational speed while decreasing power and memory usage. Rather than using FPGAs as accelerators in tandem with CPUs or GPUs, we directly encode individual neural network layers as combinational logic within FPGA hardware. Utilizing binarized neural networks minimizes the arithmetic computation required, shrinking latency to only the signal propagation delay. We evaluate size-optimization strategies and demonstrate network compression via weight quantization and weight-model unification, achieving 96% of the accuracy of baseline MNIST digit classification models while using only 3% of the memory. We further achieve 86% decrease in model footprint, 8mW dynamic power consumption, and $< 9\text{ns}$ latency, validating the versatility and capability of feature-strength-based pruning approaches for binarized neural networks to flexibly meet performance requirements amid application resource constraints.

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Advanced Forecasting and Analysis Techniques for Optimizing Website Performance: A Comparative Study of Time Series Models

Advanced Forecasting and Analysis Techniques for Optimizing ...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Gayathri Raikar Harshith D N. Guruprasad Dept. of Computer Science and Engineering Global Academy of Technology Bangalore India Computer Science and Engineering Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Analyzing website performance is crucial for optimizing a site's functionality, enhancing user engagement, and aligning with business goals. This evaluation focuses on essential metrics such as average engagement time per session, engagement rate, and events per session, which significantly impact user experience and overall business success. Through effective performance analysis, organizations can improve user satisfaction, boost conversion rates, and enhance their profitability and reputation. This paper reviews different approaches for analyzing these key metrics, explores their effects on user behavior and business performance, and offers best practices for optimizing website performance. By employing detailed performance analysis, businesses can make informed decisions and realize their strategic goals in the digital environment.

关键词： Measurement Reviews Profitability Time series analysis Organizations Predictive models User experience Data models Performance analysis Forecasting

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Multimodal Representation Learning:Cross-modality and Shared Representation

Multimodal Representation Learning:Cross-modality and Shared...

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2022 International Conference on Industry 4.0 Technology, I4Tech 2022

作者： Mohan, Paul Brilley, Batley C Kumar A. A, Nippun Amrita School of Engineering Dept. of Computer Science & Engineering Bengaluru India Ai and Automation Litmus 7 Kochi India

ISBN: (数字)9781665471961

ISBN: (纸本)9781665471961

Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representation of the data which used for detecting features and classifying things. As compared with learning from one representation, learning from different representations make the task easier. For example, learning something from images and their texts or learning from video and their audio produces good results than learning from one source (either image or text)or (video or audio). Learning from multiple modalities helps to achieve the target correctly within short time. This paper tries to design a Multimodal Autoencoder with minimum loss that can produce shared representation of images and its texts © 2022 IEEE.

关键词： Learning systems

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Detecting Ransomware Threats Using Machine Learning Approach

Detecting Ransomware Threats Using Machine Learning Approach

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Sanmitha Chitharanjan Shetty Rose Bastian Sarah Abdul Khader Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness.

关键词： Analytical models Accuracy Computational modeling Stacking Telecommunication traffic Very large scale integration Feature extraction Ransomware Integrated circuit modeling Random forests

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International Conference in

作者： Nowshin Tasnim Kazi Rifah Noor Mursalina Islam Mohammad Nurul Huda Iqbal H Sarker Dept. of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Dept. of Computer Science & Engineering United International University Dhaka Bangladesh Edith Cowan University Perth WA Australia

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.

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Early Prediction of Cardiovascular Diseases Using Feature Selection and Machine Learning Techniques 6

Early Prediction of Cardiovascular Diseases Using Feature Se...

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Rashme, Tamanna Yesmin Islam, Linta Jahan, Sohely Prova, Ayesha Aziz Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Barishal Dept. of Computer Science and Engineering Dhaka Bangladesh Central Women's University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665435871

cardiovascular disease is one of the most important diseases that affects the heart and blood vessels. The loss of lives is mostly linked to a lack of early disease detection, and a preemptive prediction of cardiovascular disease risk will greatly alleviate the situation. Due to the increasing amount of data growth in the health care industry, therefor Machine Learning techniques predict the disease depends on the severity of the patient's side effect. This research work proposes a model to perform early prediction of cardiovascular disease by using different machine learning algorithms, which are used for different prediction purposes. For feature selection purposes, Random Forest algorithm is used to select suitable attributes for the prediction process. The proposed model is assessed based on evaluation metrics;accuracy, precision, recall (sensitivity), f1- score, and specificity. In this exploration of predicting cardiovascular disease, the XGBoost machine learning classifier accomplished a higher rate of accuracy 75.10%. Also, this model provides a higher rate for other evaluation metrics for all the evaluation metrics 76.64%, 69.88%, 79.32%, 78.16%, and 72.84% for precision, sensitivity, specificity, and f1-score, respectively in the case of early cardiovascular diseases prediction. © 2021 IEEE.

关键词： Forecasting

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EMRChain: Electronic Medical Records Management System using Blockchain

EMRChain: Electronic Medical Records Management System using...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Arjun Rajasekharan Rani Koshy Dept. of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India Department of Computer Science and Engineering College of Engineering Trivandrum Thiruvananthapuram Kerala India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Electronic Medical Records (EMRs) are traditionally managed by central authorities, posing significant security risks such as data breaches, limited interoperability, and restricted patient control. This system leverages blockchain technology to address these issues, ensuring data remains immutable, transparent, and highly secure. Utilizing the Ethereum network and IPFS technology, our system provides a decentralized framework for storing and sharing medical records. Healthcare professionals, patients, and authorized parties can seamlessly exchange and access records through Ethereum’s decentralized structure and smart contracts, enabling automated and auditable interactions. To enhance confidentiality, all data uploaded to IPFS undergoes AES 256-bit encryption, with the encrypted Content Identifier (CID) stored on the blockchain. This ensures that intercepted CIDs cannot be accessed without the decryption key. IPFS technology facilitates decentralized storage and retrieval, eliminating the need for a centralized server and improving scalability and resilience. Patients retain full control over their health information, granting or revoking access as necessary. This system ensures secure patient identification, precise access management, and data confidentiality while allowing authorized parties to track and trace record access and modifications. Our blockchain-based EMR management system addresses the inherent risks of traditional centralized systems by providing a secure, transparent, and decentralized solution. By leveraging Ethereum and IPFS, it enhances data security, patient control, and interoperability, thus paving the way for more robust and patient-centric healthcare information management. This approach ensures that healthcare data remains secure, patients maintain control over their information, and authorized parties can effectively manage and access the records.

关键词： Data privacy Scalability Smart contracts Process control Blockchains InterPlanetary File System Servers Electronic medical records Interoperability Resilience

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems

关键词： Wireless communication Time-frequency analysis System performance Symbols New Radio Resource management Reliability Uplink Optimization Demodulation

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3D Printed Conformal MIMO Antenna with Ladder Shaped Isolating Structure for Wireless Applications

3D Printed Conformal MIMO Antenna with Ladder Shaped Isolati...

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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Kulkarni, Jayshri Patel, Upesh Upadhyaya, Trushit Anguera, Jaume Garner, Brian Li, Yang Baylor University School of Engineering and Computer Science Waco United States Charotar University of Science and Technology Gujarat India Unversitat Ramon Llull Dept. of Electronics and Telecommunication Barcelona Spain

ISBN: (纸本)9798350328264

A 3D printed conformal Multiple Input Multiple Output (MIMO) antenna is proposed for 5G sub-6GHz and Wi-Fi 6E applications. The antenna is printed on a Ninjaflex substrate and consists of two identical radiators. An innovative 3D ladder-shaped structure is used as an isolating element between the radiators to reduce mutual coupling. The identical radiators have a fan-shaped structure engraved with a circular ring to operate in a dual band at a resonance of 3.35 and 6.6GHz. The overall footprint of the 3D printed MIMO antenna is only 45 × 25 mm2 and conforms to a -10dB impedance bandwidth of 40.54% (2.95-4.45 GHz) in the lower 5G sub-6GHz frequency band and 18.32% (5.95-7.15 GHz) in the upper Wi-Fi 6E frequency band. The 3D printed MIMO antenna demonstrates excellent radiation characteristics with a gain of more than 2dBi and efficiency of more than 95% in both the dual bands. The antenna's flexibility was also tested by bending it along the X and Y-axis at a radius of 30mm with successful results. © 2023 IEEE.

关键词： 3D 5G MIMO NinjaFlex Wi-Fi 6E

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