標(biāo)題: Titlebook: Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const; Geancarlo Abich,Luciano Ost,Ricardo [打印本頁(yè)] 作者: 面臨 時(shí)間: 2025-3-21 20:01
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const影響因子(影響力)
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const影響因子(影響力)學(xué)科排名
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const網(wǎng)絡(luò)公開度
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const被引頻次
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const被引頻次學(xué)科排名
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const年度引用
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const年度引用學(xué)科排名
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const讀者反饋
書目名稱Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const讀者反饋學(xué)科排名
作者: refine 時(shí)間: 2025-3-21 20:43 作者: 船員 時(shí)間: 2025-3-22 04:09 作者: Orgasm 時(shí)間: 2025-3-22 05:27 作者: exquisite 時(shí)間: 2025-3-22 11:02
,Conclusions and?Future Work,ot affect the consistency of soft error assessment. Regarding cross-compilers, those based on LLVM appeared to be more reliable ones, with the best compiler set being the . using the . optimization flag.作者: 元音 時(shí)間: 2025-3-22 16:51
Book 2023e than 14.8 million fault injections, considering different precision bit-width configurations, optimization parameters, and processor models. The authors also evaluate the relative performance, memory utilization, and soft error reliability trade-offs analysis of different CNN models considering a 作者: 元音 時(shí)間: 2025-3-22 20:47
Early Soft Error Consistency Assessment,ware stacks running at single-core resource-constrained architectures. As mentioned before, the soft error results’ consistency regarding multi-core architectures are presented in [.] which is separate from this book as they were generated by [.].作者: Lobotomy 時(shí)間: 2025-3-22 21:33
Book 2023hors also evaluate the relative performance, memory utilization, and soft error reliability trade-offs analysis of different CNN models considering a compiler-based technique w.r.t. traditional redundancy approaches..作者: 晚來的提名 時(shí)間: 2025-3-23 03:19 作者: excrete 時(shí)間: 2025-3-23 08:40 作者: Default 時(shí)間: 2025-3-23 09:42 作者: 舔食 時(shí)間: 2025-3-23 17:30
?kologische Unternehmenspolitikomous driving, and smart healthcare [5, 6]. In this regard, this Book aims to explore, at early design phases, a consistent and extensive soft error reliability assessment of ML algorithms developed with specialised libraries that enable the execution of such applications in resource-constrained Arm作者: BABY 時(shí)間: 2025-3-23 19:57
,Beitrag der neoklassischen Umwelt?konomie,ecuting on resource-constrained IoT systems. First, Sect. 3.1 presents a review of fault injector frameworks implemented on the top of VPs. Next, Sect. 3.2 discusses some related works on soft error reliability assessment of ML models in different scopes. Finally, we distinguish this Book from the w作者: EXUDE 時(shí)間: 2025-3-23 23:05
Introduction,vements in internet protocols and the computational efficiency of emerging technologies have made communication between different devices more accessible than before [1]. Recent reports, from communication technology enterprises [2] estimate that there will be around 25–32 billion devices connected 作者: 磨坊 時(shí)間: 2025-3-24 05:11
Background in ML Models and Radiation Effects,s. First, Sect.?2.1 presents a background in ML models and the challenges to enable the execution of such models in IoT edge devices. Further, Sect.?2.2 addresses the basic concepts regarding radiation-induced errors and their impact on electronic computing system devices.作者: 機(jī)械 時(shí)間: 2025-3-24 08:33 作者: 愛國(guó)者 時(shí)間: 2025-3-24 11:08
Soft Error Assessment Methodology, multi-core systems (Sect.?.). In this sense, each fault injection module is detailed to show its usefulness, that is, RTL (Sect.?.), gem5 (Sect.?.), and Open Virtual Platform Simulator (OVPsim) (Sect.?.).作者: Original 時(shí)間: 2025-3-24 18:21
Early Soft Error Consistency Assessment,tribution lead to the [.] publication. The Sect.?. detail the case studies adopted to assess the consistency of the results considering different software stacks running at single-core resource-constrained architectures. As mentioned before, the soft error results’ consistency regarding multi-core a作者: notification 時(shí)間: 2025-3-24 19:29
,Soft Error Reliability Assessment of?ML Inference Models Executing on?Resource-Constrained IoT Edgeon of ML inference models executing on resource-constrained IoT edge devices. The underlying contribution has been published in several international conferences [.,.,.] and high quality journals [., .].作者: 壁畫 時(shí)間: 2025-3-25 01:23
,Conclusions and?Future Work,on framework. This work has investigated the soft error assessment consistency of a JIT virtual platform simulator (SOFIA) with more than 12 million fault injections considering single and multi-core Arm processor architectures. The fault injection campaigns considered different cross-compilers, sof作者: 察覺 時(shí)間: 2025-3-25 06:59
Geancarlo Abich,Luciano Ost,Ricardo ReisDescribes a virtual platform framework (i.e., SOFIA) to conduct soft error reliability assessment of CNN software.Uses novel fault injection techniques to assess the impact of CNN models running in re作者: 事先無準(zhǔn)備 時(shí)間: 2025-3-25 09:54 作者: 小丑 時(shí)間: 2025-3-25 14:02
https://doi.org/10.1007/978-3-031-18599-1software reliability; soft error analysis; Fault Injection; Machine Learning Applied to Soft Error Asse作者: 擔(dān)心 時(shí)間: 2025-3-25 15:53 作者: aviator 時(shí)間: 2025-3-25 20:46
Background in ML Models and Radiation Effects,s. First, Sect.?2.1 presents a background in ML models and the challenges to enable the execution of such models in IoT edge devices. Further, Sect.?2.2 addresses the basic concepts regarding radiation-induced errors and their impact on electronic computing system devices.作者: clarify 時(shí)間: 2025-3-26 02:18 作者: 離開可分裂 時(shí)間: 2025-3-26 07:33 作者: Fortify 時(shí)間: 2025-3-26 11:48
Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const978-3-031-18599-1Series ISSN 2690-0300 Series E-ISSN 2690-0327 作者: 構(gòu)想 時(shí)間: 2025-3-26 15:52
https://doi.org/10.1007/978-3-322-97014-5s. First, Sect.?2.1 presents a background in ML models and the challenges to enable the execution of such models in IoT edge devices. Further, Sect.?2.2 addresses the basic concepts regarding radiation-induced errors and their impact on electronic computing system devices.作者: 食草 時(shí)間: 2025-3-26 19:53
Peter Bruhns,Hands-Georg Glasemann multi-core systems (Sect.?.). In this sense, each fault injection module is detailed to show its usefulness, that is, RTL (Sect.?.), gem5 (Sect.?.), and Open Virtual Platform Simulator (OVPsim) (Sect.?.).作者: 造反,叛亂 時(shí)間: 2025-3-26 21:34
https://doi.org/10.1007/978-3-322-84893-2on of ML inference models executing on resource-constrained IoT edge devices. The underlying contribution has been published in several international conferences [.,.,.] and high quality journals [., .].作者: finite 時(shí)間: 2025-3-27 03:54
?kologische Unternehmenspolitikvements in internet protocols and the computational efficiency of emerging technologies have made communication between different devices more accessible than before [1]. Recent reports, from communication technology enterprises [2] estimate that there will be around 25–32 billion devices connected 作者: nugatory 時(shí)間: 2025-3-27 08:30 作者: 創(chuàng)造性 時(shí)間: 2025-3-27 12:49
,Beitrag der neoklassischen Umwelt?konomie,efinitions from Avi?ienis et al. [1] for fault, error, and failure. A fault is an event that may cause the internal state of the system to change, e.g., a radiation particle strike. When a fault affects the system’s internal state, it becomes an error. If the error causes a deviation of at least one作者: gorgeous 時(shí)間: 2025-3-27 16:01 作者: 來自于 時(shí)間: 2025-3-27 20:08 作者: 夜晚 時(shí)間: 2025-3-27 23:47 作者: garrulous 時(shí)間: 2025-3-28 05:45
Andrea Dagef?rde,Reinhard F. Hüttlon framework. This work has investigated the soft error assessment consistency of a JIT virtual platform simulator (SOFIA) with more than 12 million fault injections considering single and multi-core Arm processor architectures. The fault injection campaigns considered different cross-compilers, sof作者: grudging 時(shí)間: 2025-3-28 08:51 作者: avarice 時(shí)間: 2025-3-28 14:09
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