首页|Studies from University of Trento Update Current Data on Robotics (Neutrons Sens itivity of Deep Reinforcement Learning Policies On Edgeai Accelerators)
Studies from University of Trento Update Current Data on Robotics (Neutrons Sens itivity of Deep Reinforcement Learning Policies On Edgeai Accelerators)
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Fresh data on Robotics are presented i n a new report. According to news reporting from Trento, Italy, by NewsRx journa lists, research stated, "Autonomous robots and their applications are becoming p opular in several different fields, including tasks where robots closely interac t with humans. Therefore, the reliability of computation must be paramount." Financial supporters for this research include Italian Ministry for University a nd Research (MUR) through the "Departments of Excellence2023-2027" Program, Euro pean Union (EU), SMART-ER Project through European Union, Coordenacao de Aperfei coamento de Pessoal de Nivel Superior (CAPES). The news correspondents obtained a quote from the research from the University o f Trento, "In this work, we measure the reliability of Google's Coral Edge tenso r processing unit (TPU) executing three deep reinforcement learning (DRL) models through an accelerated neutrons beam. We experimentally collect data that, when scaled to the natural neutron flux, account for more than 5 million years. Base d on our extensive evaluation, we quantify and qualify the radiation-induced cor ruption on the correctness of DRL. Crucially, our data show that the Edge TPU ex ecuting DRL has an error rate that is up to 18 times higher the limit imposed by international reliability standards. We found that despite the feedback and int rinsic redundancy of DRL, the propagation of the fault induces the model to fail in the vast majority of cases or the model manages to finish but reports wrong metrics (i.e., speed, final position, and reward)."
TrentoItalyEuropeEmerging Technolo giesMachine LearningNano-robotReinforcement LearningRoboticsUniversity of Trento
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