Titlebook: Early Soft Error Reliability Assessment of Convolutional Neural Networks Executing on Resource-Const; Geancarlo Abich,Luciano Ost,Ricardo

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?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

BABY

,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

EXUDE

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

磨坊

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.

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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.?.).

Original

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

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,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 [., .].

壁畫(huà)

,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