arXiv - CS - Emerging Technologies最新文献_第9页

On the Limitations of Compute Thresholds as a Governance Strategy 论计算阈值作为治理策略的局限性

arXiv - CS - Emerging Technologies Pub Date : 2024-07-08 DOI: arxiv-2407.05694

Sara Hooker

{"title":"On the Limitations of Compute Thresholds as a Governance Strategy","authors":"Sara Hooker","doi":"arxiv-2407.05694","DOIUrl":"https://doi.org/arxiv-2407.05694","url":null,"abstract":"At face value, this essay is about understanding a fairly esoteric governance\u0000tool called compute thresholds. However, in order to grapple with whether these\u0000thresholds will achieve anything, we must first understand how they came to be.\u0000This requires engaging with a decades-old debate at the heart of computer\u0000science progress, namely, is bigger always better? Hence, this essay may be of\u0000interest not only to policymakers and the wider public but also to computer\u0000scientists interested in understanding the role of compute in unlocking\u0000breakthroughs. Does a certain inflection point of compute result in changes to\u0000the risk profile of a model? This discussion is increasingly urgent given the\u0000wide adoption of governance approaches that suggest greater compute equates\u0000with higher propensity for harm. Several leading frontier AI companies have\u0000released responsible scaling policies. Both the White House Executive Orders on\u0000AI Safety (EO) and the EU AI Act encode the use of FLOP or floating-point\u0000operations as a way to identify more powerful systems. What is striking about\u0000the choice of compute thresholds to-date is that no models currently deployed\u0000in the wild fulfill the current criteria set by the EO. This implies that the\u0000emphasis is often not on auditing the risks and harms incurred by currently\u0000deployed models - but rather is based upon the belief that future levels of\u0000compute will introduce unforeseen new risks. A key conclusion of this essay is\u0000that compute thresholds as currently implemented are shortsighted and likely to\u0000fail to mitigate risk. Governance that is overly reliant on compute fails to\u0000understand that the relationship between compute and risk is highly uncertain\u0000and rapidly changing. It also overestimates our ability to predict what\u0000abilities emerge at different scales. This essay ends with recommendations for\u0000a better way forward.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Efficient Knapsack Optimization Using Probabilistic Memristor Crossbars 利用概率 Memristor 交叉条实现高能效 Knapsack 优化

arXiv - CS - Emerging Technologies Pub Date : 2024-07-05 DOI: arxiv-2407.04332

Jinzhan Li, Suhas Kumar, Su-in Yi

{"title":"Energy Efficient Knapsack Optimization Using Probabilistic Memristor Crossbars","authors":"Jinzhan Li, Suhas Kumar, Su-in Yi","doi":"arxiv-2407.04332","DOIUrl":"https://doi.org/arxiv-2407.04332","url":null,"abstract":"Constrained optimization underlies crucial societal problems (for instance,\u0000stock trading and bandwidth allocation), but is often computationally hard\u0000(complexity grows exponentially with problem size). The big-data era urgently\u0000demands low-latency and low-energy optimization at the edge, which cannot be\u0000handled by digital processors due to their non-parallel von Neumann\u0000architecture. Recent efforts using massively parallel hardware (such as\u0000memristor crossbars and quantum processors) employing annealing algorithms,\u0000while promising, have handled relatively easy and stable problems with sparse\u0000or binary representations (such as the max-cut or traveling salesman\u0000problems).However, most real-world applications embody three features, which\u0000are encoded in the knapsack problem, and cannot be handled by annealing\u0000algorithms - dense and non-binary representations, with destabilizing\u0000self-feedback. Here we demonstrate a post-digital-hardware-friendly randomized\u0000competitive Ising-inspired (RaCI) algorithm performing knapsack optimization,\u0000experimentally implemented on a foundry-manufactured CMOS-integrated\u0000probabilistic analog memristor crossbar. Our solution outperforms digital and\u0000quantum approaches by over 4 orders of magnitude in energy efficiency.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing 最新成果：强化神经网络：利用随机计算防范恶意攻击

arXiv - CS - Emerging Technologies Pub Date : 2024-07-05 DOI: arxiv-2407.04861

Faeze S. Banitaba, Sercan Aygun, M. Hassan Najafi

引用次数: 0

Resistive Memory for Computing and Security: Algorithms, Architectures, and Platforms 用于计算和安全的电阻式内存：算法、架构和平台

arXiv - CS - Emerging Technologies Pub Date : 2024-07-04 DOI: arxiv-2407.03843

Simranjeet Singh, Farhad Merchant, Sachin Patkar

引用次数: 0

Quantum Serverless Paradigm and Application Development using the QFaaS Framework 量子无服务器范式和使用 QFaaS 框架的应用程序开发

arXiv - CS - Emerging Technologies Pub Date : 2024-07-03 DOI: arxiv-2407.02828

Hoa T. Nguyen, Bui Binh An Pham, Muhammad Usman, Rajkumar Buyya

引用次数: 0

Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization 使用知识提炼和量化的边缘设备统一异常检测方法

arXiv - CS - Emerging Technologies Pub Date : 2024-07-03 DOI: arxiv-2407.02968

Sushovan Jena, Arya Pulkit, Kajal Singh, Anoushka Banerjee, Sharad Joshi, Ananth Ganesh, Dinesh Singh, Arnav Bhavsar

{"title":"Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization","authors":"Sushovan Jena, Arya Pulkit, Kajal Singh, Anoushka Banerjee, Sharad Joshi, Ananth Ganesh, Dinesh Singh, Arnav Bhavsar","doi":"arxiv-2407.02968","DOIUrl":"https://doi.org/arxiv-2407.02968","url":null,"abstract":"With the rapid advances in deep learning and smart manufacturing in Industry\u00004.0, there is an imperative for high-throughput, high-performance, and fully\u0000integrated visual inspection systems. Most anomaly detection approaches using\u0000defect detection datasets, such as MVTec AD, employ one-class models that\u0000require fitting separate models for each class. On the contrary, unified models\u0000eliminate the need for fitting separate models for each class and significantly\u0000reduce cost and memory requirements. Thus, in this work, we experiment with\u0000considering a unified multi-class setup. Our experimental study shows that\u0000multi-class models perform at par with one-class models for the standard MVTec\u0000AD dataset. Hence, this indicates that there may not be a need to learn\u0000separate object/class-wise models when the object classes are significantly\u0000different from each other, as is the case of the dataset considered.\u0000Furthermore, we have deployed three different unified lightweight architectures\u0000on the CPU and an edge device (NVIDIA Jetson Xavier NX). We analyze the\u0000quantized multi-class anomaly detection models in terms of latency and memory\u0000requirements for deployment on the edge device while comparing\u0000quantization-aware training (QAT) and post-training quantization (PTQ) for\u0000performance at different precision widths. In addition, we explored two\u0000different methods of calibration required in post-training scenarios and show\u0000that one of them performs notably better, highlighting its importance for\u0000unsupervised tasks. Due to quantization, the performance drop in PTQ is further\u0000compensated by QAT, which yields at par performance with the original 32-bit\u0000Floating point in two of the models considered.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing DRLQ：基于深度强化学习的量子云计算任务分配

arXiv - CS - Emerging Technologies Pub Date : 2024-07-03 DOI: arxiv-2407.02748

Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya

{"title":"DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing","authors":"Hoa T. Nguyen, Muhammad Usman, Rajkumar Buyya","doi":"arxiv-2407.02748","DOIUrl":"https://doi.org/arxiv-2407.02748","url":null,"abstract":"The quantum cloud computing paradigm presents unique challenges in task\u0000placement due to the dynamic and heterogeneous nature of quantum computation\u0000resources. Traditional heuristic approaches fall short in adapting to the\u0000rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a\u0000novel Deep Reinforcement Learning (DRL)-based technique for task placement in\u0000quantum cloud computing environments, addressing the optimization of task\u0000completion time and quantum task scheduling efficiency. It leverages the Deep Q\u0000Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a\u0000dynamic task placement strategy. This approach is one of the first in the field\u0000of quantum cloud resource management, enabling adaptive learning and\u0000decision-making for quantum cloud environments and effectively optimizing task\u0000placement based on changing conditions and resource availability. We conduct\u0000extensive experiments using the QSimPy simulation toolkit to evaluate the\u0000performance of our method, demonstrating substantial improvements in task\u0000execution efficiency and a reduction in the need to reschedule quantum tasks.\u0000Our results show that utilizing the DRLQ approach for task placement can\u0000significantly reduce total quantum task completion time by 37.81% to 72.93% and\u0000prevent task rescheduling attempts compared to other heuristic approaches.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In-Memory Mirroring: Cloning Without Reading 内存镜像不读取的克隆

arXiv - CS - Emerging Technologies Pub Date : 2024-07-03 DOI: arxiv-2407.02921

Simranjeet Singh, Ankit Bende, Chandan Kumar Jha, Vikas Rana, Rolf Drechsler, Sachin Patkar, Farhad Merchant

{"title":"In-Memory Mirroring: Cloning Without Reading","authors":"Simranjeet Singh, Ankit Bende, Chandan Kumar Jha, Vikas Rana, Rolf Drechsler, Sachin Patkar, Farhad Merchant","doi":"arxiv-2407.02921","DOIUrl":"https://doi.org/arxiv-2407.02921","url":null,"abstract":"In-memory computing (IMC) has gained significant attention recently as it\u0000attempts to reduce the impact of memory bottlenecks. Numerous schemes for\u0000digital IMC are presented in the literature, focusing on logic operations.\u0000Often, an application's description has data dependencies that must be\u0000resolved. Contemporary IMC architectures perform read followed by write\u0000operations for this purpose, which results in performance and energy penalties.\u0000To solve this fundamental problem, this paper presents in-memory mirroring\u0000(IMM). IMM eliminates the need for read and write-back steps, thus avoiding\u0000energy and performance penalties. Instead, we perform data movement within\u0000memory, involving row-wise and column-wise data transfers. Additionally, the\u0000IMM scheme enables parallel cloning of entire row (word) with a complexity of\u0000$mathcal{O}(1)$. Moreover, our analysis of the energy consumption of the\u0000proposed technique using resistive random-access memory crossbar and\u0000experimentally validated JART VCM v1b model. The IMM increases energy\u0000efficiency and shows 2$times$ performance improvement compared to conventional\u0000data movement methods.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralized Intelligence Network (DIN) 分散式情报网络（DIN）

arXiv - CS - Emerging Technologies Pub Date : 2024-07-02 DOI: arxiv-2407.02461

Abraham Nash

{"title":"Decentralized Intelligence Network (DIN)","authors":"Abraham Nash","doi":"arxiv-2407.02461","DOIUrl":"https://doi.org/arxiv-2407.02461","url":null,"abstract":"Decentralized Intelligence Network (DIN) addresses the significant challenges\u0000of data sovereignty and AI utilization caused by the fragmentation and siloing\u0000of data across providers and institutions. This comprehensive framework\u0000overcomes access barriers to scalable data sources previously hindered by silos\u0000by leveraging: 1) personal data stores as a prerequisite for data sovereignty;\u00002) a scalable federated learning protocol implemented on a public blockchain\u0000for decentralized AI training, where data remains with participants and only\u0000model parameter updates are shared; and 3) a scalable, trustless rewards\u0000mechanism to incentivize participation and ensure fair reward distribution.\u0000This framework ensures that no entity can prevent or control access to training\u0000on data offered by participants or determine financial benefits, as these\u0000processes operate on a public blockchain with an immutable record and without a\u0000third party. It supports effective AI training, allowing participants to\u0000maintain control over their data, benefit financially, and contribute to a\u0000decentralized, scalable ecosystem that leverages collective AI to develop\u0000beneficial algorithms.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"137 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review 具有深度学习和自然语言功能的辅助图像注释系统：综述

arXiv - CS - Emerging Technologies Pub Date : 2024-06-28 DOI: arxiv-2407.00252

Moseli Mots'oehli

{"title":"Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review","authors":"Moseli Mots'oehli","doi":"arxiv-2407.00252","DOIUrl":"https://doi.org/arxiv-2407.00252","url":null,"abstract":"While supervised learning has achieved significant success in computer vision\u0000tasks, acquiring high-quality annotated data remains a bottleneck. This paper\u0000explores both scholarly and non-scholarly works in AI-assistive deep learning\u0000image annotation systems that provide textual suggestions, captions, or\u0000descriptions of the input image to the annotator. This potentially results in\u0000higher annotation efficiency and quality. Our exploration covers annotation for\u0000a range of computer vision tasks including image classification, object\u0000detection, regression, instance, semantic segmentation, and pose estimation. We\u0000review various datasets and how they contribute to the training and evaluation\u0000of AI-assistive annotation systems. We also examine methods leveraging\u0000neuro-symbolic learning, deep active learning, and self-supervised learning\u0000algorithms that enable semantic image understanding and generate free-text\u0000output. These include image captioning, visual question answering, and\u0000multi-modal reasoning. Despite the promising potential, there is limited\u0000publicly available work on AI-assistive image annotation with textual output\u0000capabilities. We conclude by suggesting future research directions to advance\u0000this field, emphasizing the need for more publicly accessible datasets and\u0000collaborative efforts between academia and industry.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0