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proceedings of the 38th acm/sigapp symposium on applied computing, SAC 2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Comp...

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38th Annual acm symposium on applied computing, SAC 2023

ISBN: (纸本)9781450395175

the proceedings contain 259 papers. the topics discussed include: EdGCon: auto-assigner of iconicity ratings grounded by lexical properties to aid in generation of technical gestures;a semantic evidence-based approach to continuous cloud service certification;digital forgetting using key decay;on the measurement of performance metrics for virtualization-enhanced architectures;COSTA: a cost-driven solution for migrating applications in multi-cloud environments;towards a high-interaction physics-aware honeynet for industrial control systems;enhancing polar codes efficiency on 3D flash memory by exploiting multiple error variations;traffic intersections as agents: a model checking approach for analyzing communicating agents;an extensible framework for implementing byzantine fault-tolerant protocols;detection of adversarial attacks by observing deep features with structured data algorithms;and proof of swarm based ensemble learning for federated learning applications.

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proceedings of the 37th acm/sigapp symposium on applied computing, SAC 2022

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Comp...

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37th acm/sigapp symposium on applied computing, SAC 2022

ISBN: (纸本)9781450387132

the proceedings contain 272 papers. the topics discussed include: residual size is not enough for anomaly detection: improving detection performance using residual similarity in multivariate time series;CSP specification and verification of relay-based railway interlocking systems;on enduring more data through enabling page rewrite capability on multi-level-cell flash memory;RAM: exploiting restrained and approximate management for enabling neural network training on NVM-based systems;CIIA: critical infrastructure impact assessment;ontology-based user privacy management in the smart grid;efficient and scalable geographical peer matching for P2P energy sharing communities;planetary digital twin: a case study in aquaculture;a framework for representing internet of things security and privacy policies and detecting potential problems;and energy-efficient fog computing-enabled data transmission protocol in tactile internet-based applications.

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39th Annual acm symposium on applied computing, SAC 2024

39th Annual ACM Symposium on Applied Computing, SAC 2024

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39th Annual acm symposium on applied computing, SAC 2024

ISBN: (纸本)9798400702433

the proceedings contain 255 papers. the topics discussed include: coordination of marine multi robot systems with communication constraints;detection of war-caused agricultural field damages using sentinel-2 satellite data with machine learning and anomaly detection;real-time 3D registration and fusion with SRAM-based analog in-memory computing;enhancing safety in cyber-physical systems through runtime enforcement;least privilege persistent-storage access in web browsers;rescuing QUIC flows from countermeasures against UDP flooding attacks;avoiding empty instances and offset drifts of basic sequencer tasks in automotive operating system;trustful coopetitive infrastructures for the new space exploration era;knowledge base grounded pre-trained language models via distillation;and CoSMo: a multilingual modular language for content selection modelling.

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Student Research Abstract: Real-Time 3D Registration and Fusion with SRAM-based Analog In-Memory computing 24

Student Research Abstract: Real-Time 3D Registration and Fus...

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39th Annual acm symposium on applied computing, SAC 2024

作者： Liu, Xin-You Department of Computer Sciences and Information Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400702433

In-memory computing systems are designed to offload workloads without transmitting tasks between CPU or GPU, reducing energy consumption. 3D sensors generate events of interest, usually powered by harvested energy or prolonged battery lifetimes. this work designed a three-dimensional (3D) sensing system using analog SRAM-based CIMs macro for storage and data processing to identify the subjects. the proposed neural network training scheme embedded the uncertainty model to tackle ADC non-deterministic thermal noise error. the design leverages the multiplication-accumulation (MAC) operation with limited precision on SRAM CIM macro, accelerating computation such as graph convolution, attention mechanism, and k nearest high dimensional feature vectors in Euclidean distance for the proposed registration pipeline. the experiment shows a reduction in the model size by up to 27 times, reduces the energy consumption by at least 100 times, and shortens the inference time by 297 times. © 2024 Copyright held by the owner/author(s).

关键词： Energy utilization

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AFeCAM: An Energy Efficient Analog 1FeFET Content Addressable Memory 24

AFeCAM: An Energy Efficient Analog 1FeFET Content Addressabl...

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34th Great Lakes symposium on VLSI (GLSVLSI)

作者： Moon, Sabrina Hassan Reis, Dayane Univ S Florida Tampa FL 33620 USA

ISBN: (纸本)9798400706059

Content Addressable Memories (CAMs) have the ability to perform parallel searches, significantly enhancing the computational efficiency of computing-in-Memory (CiM) architectures. CAMs can be employed in various areas, including DNA sequence analysis, IP routing, etc. Meanwhile, Ferroelectric Field Effect Transistors (Fe-FETs) offer a highly efficient solution for CAM implementation due to their high I-on/I-off ratio, voltage-driven write mechanism, and non-volatility. We propose a 1FeFET analog CAM design (AFeCAM), which minimizes the area footprint compared to previous FeFET-based CAMs, making it possible to conduct searches with minimal search energy in data-intensive applications. Our AFeCAM design is ultra-compact, scalable, and uses a voltage comparator sense amplifier to detect matches and mismatches in an energy-efficient manner.

关键词： computing-in-Memory Emerging technologies FeFETs

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Can Stochastic computing Truly Tolerate Bit Flips? 24

Can Stochastic Computing Truly Tolerate Bit Flips?

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34th Great Lakes symposium on VLSI (GLSVLSI)

作者： Wang, Yutong Ni, Zhaojun Liu, Siting ShanghaiTech Univ Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom A Shanghai Peoples R China

ISBN: (纸本)9798400706059

Transient bit flips may cause unexpected behaviors in digital arithmetic circuits, and thus computation errors. It is generally believed that stochastic computing (SC) can tolerate bit flips due to its unique number representation format. In SC, a value is encoded by a random bit stream using the probability of 1s in the bit stream. therefore, one bit flip changes the probability by a small number given a long enough bit stream, and the computation results might not change much. this is verified in previous works mostly through empirical studies. However, we found that bit flips actually change the probability of a stochastic bit stream by a predictable value. this is formulated by considering the bit flips as Bernoulli events. the distorted probability of the bit stream considering bit flips is derived and the SC bit flip models for various stochastic circuits are proposed. the error recovery based on the formulation is also proposed. the SC bit flip model and error recovery scheme are verified on basic SC elements such as the SC multipliers and an FSM-based stochastic circuit. they are then applied to a complex SC system computing a neural network. the results show that the accuracy of MNIST digit recognition can be recovered even with a high bit flip rate of 10%.

关键词： Stochastic computing Bit flip Soft error Fault tolerant Error recovery

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Feature-driven Approximate computing for Wearable Health Monitoring Systems 24

Feature-driven Approximate Computing for Wearable Health Mon...

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34th Great Lakes symposium on VLSI (GLSVLSI)

作者： Chennagouni, Nishanth Kajol, Mashrafi Chen, Diliang Xu, Dongpeng Yu, Qiaoyan Univ New Hampshire Durham NH 03824 USA

ISBN: (纸本)9798400706059

Real-time health monitoring systems generate a large volume of sensing data, requiring tremendous processing time and storage space. Orthogonal to existing approximate computing mechanisms, thiswork proposes a Feature-Driven Approximation (FDApx) method to address the pressing need for fast data processing and a limited storage budget in wearable health monitoring devices. the proposed FDApx method reverses the features interested in high-level applications to derive approximation thresholds to retain feature-critical information, rather than aimlessly storing and transmitting all raw data. Case studies in an insole sensing system for fall risk assessment show that FDApx can reduce the data size by up to 87% over raw data and up to 85% over 2-bit precision reduction-based approximation. the approximation from FDApx only results in up to a 2% deviation in swing time;in contrast, the approximation based on precision reduction causes a 30% deviation in the same gait feature(1).

关键词： Approximate computing wearable sensor data processing.

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Word2HyperVec: From Word Embeddings to Hypervectors for Hyperdimensional computing 24

Word2HyperVec: From Word Embeddings to Hypervectors for Hype...

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34th Great Lakes symposium on VLSI (GLSVLSI)

作者： Ayar, Alaaddin Goktug Aygun, Sercan Najafi, M. Hassan Margala, Martin Univ Louisiana Lafayette Lafayette LA 70504 USA

ISBN: (纸本)9798400706059

Word-aware sentiment analysis has posed a significant challenge over the past decade. Despite the considerable efforts of recent language models, achieving a lightweight representation suitable for deployment on resource-constrained edge devices remains a crucial concern. this study proposes a novel solution by merging two emerging paradigms, the Word2Vec language model and Hyperdimensional computing, and introduces an innovative framework named Word2HyperVec. Our framework prioritizes model size and facilitates low-power processing during inference by incorporating embeddings into a binary space. Our solution demonstrates significant advantages, consuming only 2.2 W, up to 1.81x more efficient than alternative learning models such as support vector machines, random forest, and multi-layer perceptron.

关键词： Sentiment analysis

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Event-Driven Sensing and Embedded Neuromorphic Platforms for Gamma Radiation Monitoring 24

Event-Driven Sensing and Embedded Neuromorphic Platforms for...

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34th Great Lakes symposium on VLSI (GLSVLSI)

作者： Witherspoon, Brett Young, Aaron Oak Ridge Natl Lab Sensors & Elect Grp Oak Ridge TN 37831 USA Oak Ridge Natl Lab Architectures & Performance Grp Oak Ridge TN USA

ISBN: (纸本)9798400706059

this work will present an embedded neuromorphic platform developed for long-term unintended gamma radiation monitoring. We describe the hardware architecture and supporting software developed to demonstrate neuromorphic computing for applications where ultra-low power and always-on sensing are required. this is followed by a discussion of our current work on an improved platform that integrates both event-driven vision and gamma-ray spectroscopy sensors for nuclear safeguards applications. Finally, future research directions toward event-driven sampling techniques to integrate analog-to-information reduction into the sensing electronics are proposed.

关键词： spiking neural networks neuromorphic computing radiation detection anomaly detection bio-inspired computing event-driven sampling

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SAC 2023: Track Editorial [IoT and Edge computing] 38

SAC 2023: Track Editorial [IoT and Edge Computing]

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38th Annual acm symposium on applied computing, SAC 2023

作者： Min, Hong Baidya, Sabur Gachon University Korea Republic of University of Louisville United States

ISBN: (纸本)9781450395175

the rapid advancement in electronics, embedded systems, wireless communications, computer software applications and artificial intelligence is expanding the use of the Internet-of-things (IoT) in many application domains. However, with the prevalence of IoT, the problem of how to manage the vast volumes of data, orchestrate and manage the wide variety and type of data generated, and erratic generation patterns is becoming increasingly challenging and difficult issues. Additionally, with the advancement of artificial intelligence (AI), many emerging IoT applications are often compute-intensive and have stringent performance constraints. Edge computing is proposed as a potential solution to mitigate the problem as the edge computing brings data processing and other computing needs as close to the smart devices as possible, thus reducing latency and energy consumption and other potential benefits. the edge computing reduces the cascade of potential bandwidth bottlenecks and processes the data that matters, keeping it close to the source. the balance between edge servers and cloud servers is an important issue in this field. © 2023 Association for computing Machinery. All rights reserved.

关键词： Edge computing

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