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End-to-end Integration of OpenTitan Security Features in a Pure RISC-V SoC

End-to-end Integration of OpenTitan Security Features in a P...

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international conference on Synthesis, modeling, Analysis and simulation Methods and Applications to Circuit Design (SMACD)

作者： Alberto Musa Emanuele Parisi Luca Barbierato Andrea Acquaviva Francesco Barchi Università di Bologna Bologna Italy Politecnico di Torino Torino Italy

ISBN: (数字)9798350351927

ISBN: (纸本)9798350351934

The RISC-V open-source instruction set architecture (ISA) is gaining popularity in the realm of new processor development. Its maturity allows implementation even in scenarios where security plays a key role. OpenTitan (OT), an open-source silicon Root of Trust (RoT) designed specifically for secure embedded environments is a significant example of this adoption. This study focuses on integrating OT into a System-on-Chip (SoC) exclusively featuring RISC-V architectures. In this configuration, OT serves as a secure co-processor, leveraging its cryptographic accelerators to enhance the performance of cryptographic workloads. A noteworthy aspect of this implementation is the intentional preservation of strong isolation in computation and memory, an essential feature to protect sensitive data, including cryptographic keys and intermediate results of cryptographic tasks. The integration of OT into a complete RISC-V SoC was executed and characterized on an FPGA. The FPGA runs the entire SoC which leverages a specialized communication system between two domains: a Host domain and a Safe domain. The Host domain features a CVA6 processor running Linux, while the Safe domain houses the OT system. During system characterization, the delays introduced by the communication and synchronization system between the Host and Safe domains were measured, along with the performance of cryptographic operations conducted in the Safe domain. Results demonstrate the effectiveness of OT HW/SW integration, compensating for the overhead introduced by the communication and synchronization system between the two domains. This makes the proposed implementation sustainable for various application cases and facilitates its integration into embedded and cyber-physical systems based on secure open-hardware architectures.

关键词： Linux Instruction sets computer architecture Silicon System-on-chip Cryptography Synchronization Integrated circuit modeling Field programmable gate arrays Hardware acceleration

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Deep Learning-Driven Acceleration of Stochastic Gradient Methods for Well Location Optimization Under Uncertainty

Deep Learning-Driven Acceleration of Stochastic Gradient Met...

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2024 SPE Annual Technical conference and Exhibition, ATCE 2024

作者： Eltahan, Esmail Sepehrnoori, Kamy Alpak, Faruk Omer The University of Texas at Austin AustinTX United States Shell International Exploration and Production Inc HoustonTX United States

ISBN: (纸本)9781959025375

We have developed the deep-learning-accelerated-gradient (DLAG) algorithm, a novel solution for well location optimization (WLO) problems that leverages data collected from the explored parameter space to accelerate optimization. During optimization, we collect data to train a deep neural network (NN), creating a map from well-location parameters to the objective function. We utilize the analytical gradient of the NN, resulting in an effective search direction while saving the computational cost associated with stochastic-gradient perturbations. Our method features a novel NN architecture, the Spatial Pairwise Interaction Network (SPINet) with independent and contextual neural pathways (NPs), designed to capture the primary well characteristics, and its complex interactions with the neighboring wells. For the contextual NP, we explore using the popular Attention mechanism and simpler mechanism with weight-sharing Multilayer Perceptron (MLP) layers. To evaluate the architectures, we designed the Bird Ensemble (BE) test problem which resembles the structure of the WLO problem. The comparison between DL architectures reveals that the NP with a weight-sharing mechanism has superior performance in terms of MSE error and gradient accuracy. The weight-sharing structure allows the model to efficiently model relationships with shared parameters while maintaining invariance to input permutations. For the test function, utilizing DLAG drastically improves computational efficiency, reducing the number of function evaluations required to achieve the same level of optimization by one to two orders of magnitude. Following validation of this test problem, we successfully applied our method to optimizing locations of injection and production wells in the Egg reservoir model. To alleviate random artifacts and the inevitable possibility of encountering local minima, we conducted 20 iterations of the WLO problems both with and without DLAG. The results reveal that, on average, our DLAG

关键词： Stochastic systems

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Comprehensive Analysis and Optimization of High-Speed Multiplication architectures and RF Energy Harvesting Techniques for Low-Power systems

Comprehensive Analysis and Optimization of High-Speed Multip...

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international conference on computer and Communications Technologies (ICCCT)

作者： Usha S M. Kanthimathi Saravanan R K Vasanth K Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering (Internet of Things) Sri Sairam Engineering College Chennai Tamil Nadu

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Efficient multipliers and Radio Frequency (RF) energy harvesting circuits are important in improving the performance of low-power wireless applications and embedded systems. In this study, we explore two important areas: the enhancement of multipliers, specifically Wallace Tree and Vedic multipliers, for improving computation efficiency in digital systems, and the development of a high-performance RF energy harvesting circuit using Dickson Voltage Multipliers (DVM) designed for 915 MHz operation. The expected changes include Carry Select Adder (CSLA) in the Wallace multiplier and advanced impedance matching methods in RF energy harvesting which helps in achieving a considerable improvement in speed, power consumption, and conversion efficiency. Field Programmable Gate Arrays (FPGA) and simulation results show that the modified designs outperform traditional systems, making them appropriate for applications in the Internet of Things (IoT), wearable devices, and other low-power systems.

关键词： Radio frequency Wireless communication Impedance matching simulation Energy harvesting Internet of Things Voltage multipliers Wearable devices Field programmable gate arrays Adders

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MBConvNet-CNS: A Lightweight Convolutional Neural Network (CNN) Architecture for Plant Disease Detection

MBConvNet-CNS: A Lightweight Convolutional Neural Network (C...

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Intelligent systems and embedded Design (ISED), international conference on

作者： Shikhar Burman Nilabh Pandey Chetna Srivastava Manas Ranjan Prusty School of Computer Science and Engineering Vellore Institute of Technology Chennai INDIA Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Plant diseases pose a big threat to agricultural productivity worldwide, so their detection technique should be strong and efficient. Classically, plant disease identifications are time-consuming and usually require expert knowledge; this makes the use of large-scale agriculture difficult. The following paper presents a review of how the use of lightweight CNN models can be used for real-time plant disease detection with models that are computationally efficient enough to be deployed on edge devices such as smartphones and drones. We shall propose a novel model based on state-of-the-art architectures comprising the likes of MobileNet, EfficientNet, Ghost Modules, and Squeeze-and-Excitation blocks that achieve high accuracy for plant disease identification with a low computation footprint. The proposed model was applied to different datasets of images of plant leaves. Experimental results proved it at par to the previous heavyweight and lightweight models based on deep learning for accuracy and time consumed. These findings point out that the correct integration of lightweight CNN architectures and innovative modules shall yield a feasible solution for effective management of plant diseases, making timely interventions possible for reducing crop losses.

关键词： Plant diseases Adaptation models Accuracy Computational modeling Crops computer architecture Computational efficiency Complexity theory Convolutional neural networks Smart phones

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Empirical Study of Far-Field Crop Quality Examination Models: A Numerical Outlook

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international conference on modeling, simulation and Optimization, CoMSO 2021

作者： Dhande, Akshay Malik, Rahul Department of Electronics and Telecommunication Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9789811908354

Analysis of crop quality from image data is a multi-domain task, which includes effective image capture, pre-processing, fusion, segmentation, feature extraction, selection of optimum features, classification and post-processing. In order to perform these tasks with utmost efficiency, several architectures have been proposed by researchers and satellite image processing system designers. These architectures vary widely in terms of performance parameters like accuracy, response time, applicability to different types of imagery, image data requirement, etc. For instance, architectures that are well suited for cotton crop analysis do not give good accuracy for other crop types, while generalized architectures that have high accuracy, underperform when applied to specialized crop types. Moreover, selection of algorithms at micro-level is also difficult due to a large variety of architectures available for each of the internal tasks. In order to reduce this ambiguity, the underlying text analyzes various state-of-the-art recent models and architectures that are proposed for high efficiency crop analysis using satellite image processing. The analysis is supported by statistical comparison and recommendations for these architectures w.r.t. their field of application, and desired system performance capabilities. This text also recommends optimizations to the reviewed architectures in order to further improve their parametric performance and large scale deploy-ability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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modeling and simulation Verification of Operating Mode Switching of Train Control System Based on Train-to-Train Communication 24th

Modeling and Simulation Verification of Operating Mode Swi...

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24th international conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Li, Qiang Liao, Ian Wen, Sheng Xiang, Yang Lanzhou Jiaotong University Lanzhou China Swinburne University of Technology Melbourne Australia Top-Rail Service Pittsburg United States

ISBN: (纸本)9789819615506

The train control system needs to accommodate various operational scenarios through the design of distinct working modes, each triggered by changing input conditions. These modes correspond to specific speed limits and safety requirements, ensuring the safety, efficiency, and continuity of train operations. The future of next-generation urban rail transit lies in train control systems based on train-to-train communication. Due to structural changes, traditional CBTC (Communication-Based Train Control) model designs are no longer applicable, and there is currently no comprehensive model design available. This paper examines the mode-switching function and processes appropriate for train-to-train communication control systems. A new mode definition and switching scheme is proposed. In the event of train-to-train communication failure, the train switches to a backup operation mode using train-to-ground communication to acquire position information from ground equipment. To describe this transition, we use the theory of Colored Petri Nets to establish a mode-switching model based on Hierarchical Timed Colored Petri Nets (HTCPN). The model effectively transitions trains from train-to-train mode (T2T) to train-to-ground mode (T2G) under failure conditions. We studied the impact of different operational intervals in the backup mode on system performance through simulations, which show that trains can effectively switch to train-to-ground mode during communication failures. Analysis of successful mode switches and switching times under different backup mode intervals reveals that the success rate increases with longer intervals. An interval of 240 s minimizes train operation delays to within 3 min. The HTCPN model established serves as a reference for verifying and analyzing other multi-mode train control systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Petri nets

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Optimized Deep Learning Models For Edge Computing: A Comparative Study on Raspberry PI4 For Real-Time Plant Disease Detection

Optimized Deep Learning Models For Edge Computing: A Compara...

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international conference on embedded & Distributed systems (EDiS)

作者： Abdelmadjid SAAD Abdeldjallil AIDI Abou El Hassan BENYAMINA Computer Science Department Laboratory of Parallel Embedded Architectures and Intensive Computing University of Oran 1 Ahmed Ben Bella Oran Algeria Computer Science Department Laboratory of Mathematics and Computer Science University of Tiaret Ibn Khaldoun Tiaret Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519889

In the pursuit of precision agriculture, the integration of artificial intelligence (AI) for real-time plant disease detection holds a significant promise. This study investigates the application of edge computing on a Raspberry Pi 4 platform to keep the performance of three optimized deep learning models: GoogLeNet, ResNet and XceptionNet, in detecting plant diseases using the new plant diseases dataset. The goal seeks to bridge the gap between theoretical advancements in deep learning and practical applications in precision agriculture, shedding light on the trade-offs between model complexity, computational demands, and diagnostic accuracy. Additionally, we provide a lightweight web application that interfaces with the trained models, exemplifying how edge computing can democratize access to AI tools, making them accessible to farmers and researchers. Our findings show that converting the models to TensorFlow Lite significantly reduces model size and inference time while maintaining similar performance, crucial for applications on resource-limited devices like the Raspberry Pi 4.

关键词： Deep learning Precision agriculture Performance evaluation Plant diseases Computational modeling Image edge detection Real-time systems Artificial intelligence Edge computing Farming

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DATCloud: A Model-Driven Framework for Multi-Layered Data-Intensive architectures

DATCloud: A Model-Driven Framework for Multi-Layered Data-In...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Moamin Abughazala Henry Muccini DISIM Department University of L'Aquila L'Aquila Italy Department of Information Technology An Najah N. University Nablus Palestine

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

The complexity of multi-layered, data-intensive systems demands frameworks that ensure flexibility, scalability, and efficiency. DATCloud is a model-driven framework designed to facilitate the modeling, validation, and refinement of multi-layered architectures, addressing scalability, modularity, and real-world requirements. By adhering to ISO/IEC/IEEE 42010 standards, DATCloud leverages structural and behavioral meta-models and graphical domain-specific languages (DSLs) to enhance reusability and stakeholder communication. Initial validation through the VASARI system at the Uffizi Gallery demonstrates a 40% reduction in modeling time and a 32% improvement in flexibility compared to manual methods. While effective, DATCloud is a work in progress, with plans to integrate advanced code generation, simulation tools, and domain-specific extensions to further enhance its capabilities for applications in healthcare, smart cities, and other data-intensive domains.

关键词： Software architecture Smart cities Scalability ISO Standards computer architecture Medical services Manuals Data models Stakeholders Standards

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Examining The Performance of Sense Amplifiers for In-Memory Computing systems

Examining The Performance of Sense Amplifiers for In-Memory ...

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modeling, simulation & Intelligent Computing (MoSICom), international conference on

作者： Sreeja S Kumar Jagadish Nayak Dept. of Electrical&Electronics Engineering BITS Pilani Dubai campus Dubai UAE

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

The principle of in-memory computing offers a highly promising framework that may effectively tackle the discrepancy between memory and computing, leading to exceptionally fast and energy-efficient data processing. The sense amplifier serves a vital role in the realm of in-memory computer architectures. The effectiveness and energy efficiency of in-memory computing systems are heavily reliant on the suitability of their sense amplifiers. This paper offers a comprehensive insight into the main goal of pinpointing sense amplifiers designed specifically for in-memory computing applications. It involves an examination of the requisites and inherent challenges linked to in-memory computing. This encompasses various factors, such as delay, energy efficiency, and resilience to process changes. A careful examination was conducted to analyze the intricate trade-offs associated with power consumption, energy efficiency, and processing speed. The considerations play a pivotal function in providing designers with the essential knowledge and confidence to proficiently include sense amplifiers into diverse in-memory computing systems. This paper presents a systematic approach to selecting and constructing sense amplifiers to enhance our understanding of in-memory computing. We tailor our approach to match the distinct requirements of different inmemory computing applications. The results gained are anticipated to have a substantial influence on the progression of memory-centric computing systems in the forthcoming years.

关键词： Technological innovation Systematics Power demand computer architecture In-memory computing Energy efficiency Stability analysis Transistors Logic Resilience

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Towards a Dynamic Composability Approach for using Heterogeneous systems in Remote Sensing 18

Towards a Dynamic Composability Approach for using Heterogen...

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IEEE 18th international conference on E-Science (E-Science)

作者： Altintas, Ilkay Perez, Ismael Mishin, Dmitry Trouillaud, Adrien Irving, Christopher Graham, John Tatineni, Mahidhar DeFanti, Thomas Strande, Shawn Smarr, Larry Norman, Michael L. Univ Calif San Diego La Jolla CA 92093 USA Admiralty Seattle WA USA

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration. However, there is still a divide between the application development pipelines of existing supercomputing environments, and these new dynamic environments that disaggregate fluid resource pools through accessible, portable and re-programmable interfaces. New approaches for dynamic composability of heterogeneous systems are needed to further advance the data-driven scientific practice for the purpose of more efficient computing and usable tools for specific scientific domains. In this paper, we present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster. We also summarize a case study in wildfire modeling, that demonstrates the application of this new infrastructure in scientific workflows: a composed system that bridges the insights from edge sensing, AI and computing capabilities with a physics-driven simulation.

关键词： Composable systems Kubernetes Multi-Cluster Federation Artificial Intelligence Remote Sensing

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