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作者： Bishnoi, Rimpy Laxmi, Vijay Gaur, Manoj Singh Flich, Jose Trivino, Francisco Malaviya Natl Inst Technol Dept Comp Sci & Engn Jaipur 302017 Rajasthan India Univ Politecn Valencia DISCA E-46022 Valencia Spain Univ Castilla La Mancha E-13071 Ciudad Real Spain

In the Ghiribaldi et al. [2013] paper, a complete self-testing and self configuring NoC infrastructure for cost-effective MPSoCs was presented in order to make NoC architecture tolerant to faults. To overcome the complexity involved during the complete reconfiguration of routing instances in the face of most of the usual failure patterns, Ghiribaldi et al. [2013] proposed a fast self-reconfiguration algorithm. The algorithm is based on segment-based routing implemented using Logic-Based Distributed Routing (LBDR) and claimed to have handled the most common NoC faults. The purpose of this comment is to demonstrate the inconsistency of the fast self-configuration method presented in Ghiribaldi et al. [2013]. To handle inconsistency, we present the correct set of LBDR bits and also argue that complete reconfiguration of the routing instance is mandatory to handle some fault combinations. New coverage results of the fast self-reconfiguration algorithm of Ghiribaldi et al. [2013] are also presented.

关键词： Design Algorithms Reliability Network-on-Chip fault tolerance

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Efficient Deep Learning Infrastructures for embedded computing systems: A Comprehensive Survey and Future Envision

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ACM transactions on embedded computing systems 2025年第1期24卷 1-100页

作者： Luo, Xiangzhong Liu, Di Kong, Hao Huai, Shuo Chen, Hui Xiong, Guochu Liu, Weichen Nanyang Technol Univ Nanyang Singapore Norwegian Univ Sci & Technol Dept Comp Sci Trondheim Norway Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to many other downstream vision tasks, such as object detection, tracking, and segmentation. However, previous well-established DNNs, despite being able to maintain superior accuracy, have also been evolving to be deeper and wider and thus inevitably necessitate prohibitive computational resources for both training and inference. This trend further enlarges the computational gap between computation-intensive DNNs and resource-constrained embedded computing systems, making it challenging to deploy powerful DNNs in real-world embedded computing systems towards ubiquitous embedded intelligence. To alleviate this computational gap and enable ubiquitous embedded intelligence, we focus in this survey on discussing recent efficient deep learning infrastructures for embedded computing systems, spanning from training to inference, from manual to automated, from convolutional neural networks to transformers, from transformers to vision transformers, from vision models to large language models, from software to hardware, and from algorithms to applications. Specifically, we discuss recent efficient deep learning infrastructures for embedded computing systems from the lens of (1) efficient manual network design for embedded computing systems, (2) efficient automated network design for embedded computing systems, (3) efficient network compression for embedded computing systems, (4) efficient on-device learning for embedded computing systems, (5) efficient large language models for embedded computing systems, (6) efficient deep learning software and hardware for embedded computing systems, and (7) efficient intelligent applications for embedded computing systems. We also envision promising future directions and trends, which have the potential to deliver more ubiquitous embedded intelligence. We believe th

关键词： embedded computing systems embedded intelligence artificial intelli- gence efficient deep learning algorithms efficient network design efficient neural architecture search effi- cient model compression efficient on-device learning efficient large language models efficient deep learning software and hardware intelligent embedded applications

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Optimization of embedded System With Edge computing and Sleep Modes for Balance Between Energy Consumption and Cache Occupancy

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IEEE transactions ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6024-6036页

作者： Hou, Chen Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing 100871 Peoples R China PKU Changsha Inst Comp & Digital Econ Changsha 410205 Peoples R China

The paper considers the embedded system that can either compute tasks locally by itself or offload tasks to the edge server for remote computing during the running period (RP) and switch to the sleep mode to save energy once the RP ends, i.e., the idle period (IP) arrives. The tasks are stored in the cache, and the more tasks are computed during the RP, the less cache space will be occupied at the end of RP, while also leading to more energy consumption. Meanwhile, the sleep mode that the embedded system enters during the IP also influences the energy consumption. Therefore, how to make the optimal tradeoff between energy consumption and cache occupancy arises as an interesting issue. To address this issue, this paper first establishes an optimization-theoretical framework to formulate the energy consumption under the constraint of cache occupancy, then discovers the most energy-saving RP, computing mode (i.e., local or edge computing), and low-power mode. An algorithm based on our discovered theoretical results is proposed for the embedded system to minimize the energy consumption within the acceptable level of cache occupancy. Theoretical analysis and field experiments jointly verify its good performance. Note to Practitioners-This paper addresses the interesting tradeoff between energy consumption and cache occupancy in the embedded system that operates in the environments with limited available energy as well as cache space. It facilitates to improve the operation efficiency of the embedded systems in the area of Internet of Things (IoT) or Cyber-Physical systems (CPS) that employs edge computing technology to empower embedded systems with more computing capability and sleep modes to guarantee embedded systems with more energy savings, in order to minimize the accumulative energy consumption, while maintaining the cache occupancy in terms of task data bits stored within an acceptable range. Experimental investigations show that the solution proposed here outperf

关键词： embedded system low-power mode computing mode energy consumption cache occupancy

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Implementing Privacy Homomorphism with Random Encoding and Computation Controlled by a Remote Secure Server

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ACM transactions on embedded computing systems 2025年第2期24卷 1-27页

作者： Hutto, Kevin Mooney, Vincent Georgia Inst Technol Atlanta GA 30332 USA

Remote IoT devices face significant security risks due to their inherent physical vulnerability. An adversarial actor with sufficient capability can monitor the devices or exfiltrate data to access sensitive information. Remotely deployed devices such as sensors need enhanced resilience against memory leakage if performing privileged tasks. To increase the security and trust of these devices we present a novel framework implementing a privacy homomorphism which creates sensor data directly in an encoded format. The sensor data is permuted at the time of creation in a manner which appears random to an observer. A separate secure server in communication with the device provides necessary information which allows the device to perform processing on the encoded data but does not allow decoding of the result. The device transmits the encoded results to the secure server which maintains the ability to interpret the results. In this article, we show how this framework works for an image sensor calculating differences between a stream of images, with initial results showing an overhead as low as only 266% in terms of throughput when compared to computing on standard unencoded numbers such as two's complement. We further show 5,000x speedup over a recent homomorphic encryption ASIC.

关键词： Hardware security encrypted computations embedded systems

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An Operational Quality Model of embedded Software Aligned with ISO 25000

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ACM transactions on embedded computing systems 2025年第1期24卷 1-41页

作者： Argotti, Yann Kenfaoui, Yasmine Baron, Claude Abran, Alain Esteban, Philippe Renault SA Toulouse France LAAS CNRS Toulouse France INSA Toulouse Toulouse France Ecole Technol Super Montreal PQ Canada Univ Toulouse III Paul Sabatier Toulouse France

embedded systems omnipresent in everyday life and industry are mainly composed of hardware and software that must comply with a number of standards and regulations. However, there is no consensus on the quality characteristics and subcharacteristics of embedded software. This article presents the steps for modeling an operational quality model for embedded software aligned with the ISO 25000 series of quality models for traditional computer systems. From a literature review composed of 40 studies on quality modeling for embedded systems and software, 85 of the most frequent quality characteristics and subcharacteristics were first identified, including a subset of 16 referenced or cited in at least 25% of the literature. Next, the design of a quality model for embedded software aligned with the ISO 25000 series was proposed with 13 characteristics and 27 subcharacteristics. The operational aspect of this quality model for embedded software is addressed next through a set of measures and measurement functions from ISO 25000 to aggregate the results of the quantification of the characteristics and subcharacteristics. A survey involving 25 embedded software specialists is presented next to gauge, using Fleiss's Kappa criteria, their agreement with the proposed quality model. Furthermore, the computed importance weights derived from the survey participants' individual opinions were compared with those derived from an analysis of 40 embedded software studies, bolstering the credibility of the model. The results of this study suggest that the proposed quality model can serve as a framework for evaluating and understanding the quality characteristics across diverse expertise levels. Furthermore, the convergence between the survey and the literature strengthens the model's credibility by anchoring it in both established literature and practitioners' agreements.

关键词： Quality model quality characteristics measure ISO qualimetry

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LAHDC: Logic-Aggregation-Based Query for embedded Hyperdimensional computing Accelerator

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IEEE transactions ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND systems 2025年第1期44卷 119-129页

作者： Yu, Tianyang Wu, Bi Chen, Ke Zhang, Gong Liu, Weiqiang Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Coll Integrated Circuits Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Minist Ind & Informat Technol Key Lab Aerosp Integrated Circuits & Microsyst Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

With low complexity and robustness, hyperdimensional computing (HDC) has become a promising paradigm for edge-side applications. HDC employs hypervectors (generally with 2-10 K dimensions) to represent input samples, and performs logical operations in hyperdimensional space to complete perceptual tasks. Compared to deep neural network (DNN), HDC is more suitable for lightweight edge-side applications (i.e., speech, activity recognition), due to its low complexity and less computational scheduling. However, existing HDC's querying process relies on trained class hypervectors, resulting in on-chip storage and transmission overhead which limits the application of ASIC-based or FPGA-based HDC accelerators in embedded systems. In this article, a logic-aggregation-based query method called LAHDC is proposed to eliminate such overhead. In addition, an ultratiny HDC accelerator design matching LAHDC is also proposed, as well as an automated tool to search for optimal structure and generate hardware design code. Experimental results show that, compared to existing ASIC-based HDC accelerators, the proposed design reduce the area/energy by more than 95%/80%.

关键词： Logic gates Training Encoding Complexity theory Hardware Logic Design automation ASIC embedded device FPGA hyperdimensional computing (HDC) logic-aggregation-based query

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Improving Efficiency in Multi-Modal Autonomous embedded systems Through Adaptive Gating

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IEEE transactions ON COMPUTERS 2025年第2期74卷 691-704页

作者： Hou, Xiaofeng Xu, Cheng Li, Chao Liu, Jiacheng Tang, Xuehan Cheng, Kwang-Ting Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

The parallel advancement of AI and IoT technologies has recently boosted the development of multi-modal computing ((MC)-C-2) on pervasive autonomous embedded systems (AES).(MC)-C-2 takes advantage of data from different modalities such as images, audio, and text and is able to achieve notable improvements in accuracy. However, achieving these accuracy gains often comes at the cost of increased computational complexity and energy consumption. Furthermore, the presence of numerous advanced sensors in these systems significantly contributes to power consumption, exacerbating the issue of limited power resources. Collectively, these challenges pose difficulties in deploying (MC)-C-2 on small embedded devices with scarce energy resources. In this article, we propose an Adaptive Modality Gating technique called AMG for in-situ (MC)-C-2 applications. The primary objective of AMG is to conserve energy while preserving the accuracy advantages of (MC)-C-2. To achieve this goal, AMG incorporates two first-of-its-kind designs. Firstly, it introduces a novel semi-gating architecture that enables partial modality sensor power gating. Specifically, we devise the de-centralized AMG (D-AMG) and centralized AMG (C-AMG) architecture. The former buffers raw data on sensors while the latter buffers raw data on the computing board, which are suitable for different edge scenarios respectively. Secondly, it facilitates a self-initialization/tuning process on the AES, which is supported by carefully-built analytical model. Extensive evaluations demonstrate the effectiveness of AMG. It achieves a 1.6x to 3.8x throughput higher than other power management methods and improves the lifespan of AES by 10% to 280% longer within the same energy budget, while satisfying all performance and latency requirements across various scenarios.

关键词： Sensors Accuracy Computer architecture Energy consumption Sensor systems Sensor arrays Hardware embedded systems Computers Pipelines Edge AI autonomous embedded systems multi-modal DNNs modality gating architecture efficiency management

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Optimized Digital Watermarking for Robust Information Security in embedded systems

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INFORMATION 2025年第4期16卷 322-322页

作者： Mekhfioui, Mohcin El Bazi, Nabil Laayati, Oussama Satif, Amal Bouchouirbat, Marouan Kissi, Chaimaa Boujiha, Tarik Chebak, Ahmed Mohammed VI Polytech Univ Green Tech Inst GTI Benguerir 43150 Morocco Ibn Tofail Univ Natl Sch Appl Sci Kenitra 14000 Morocco

With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments.

关键词： convolutional neural network digital watermarking image protection cryptography steganography IoT embedded systems

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