Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as autonomous vehicles, demands a reliable and efficient execution on hardware. Optimized dedicated hardware accelerators are being developed to achieve this. However, the design of efficient and reliable hardware has become increasingly difficult, due to the increased complexity of modern integrated circuit technology and its sensitivity against hardware faults, such as random bit-flips. It is thus desirable to exploit optimization potential for error resilience and efficiency also at the algorithmic side, e.g., by optimizing the architecture of the DNN. Since there are numerous design choices for the architecture of DNNs, with partially opposing effects on the preferred characteristics (such as small error rates at low latency), multi-objective optimization strategies are necessary. In this paper, we develop an evolutionary optimization technique for the automated design of hardware-optimized DNN architectures. For this purpose, we derive a set of easily computable objective functions, which enable the fast evaluation of DNN architectures with respect to their hardware efficiency and error resilience solely based on the network topology. We observe a strong correlation between predicted error resilience and actual measurements obtained from fault injection simulations. Furthermore, we analyze two different quantization schemes for efficient DNN computation and find significant differences regarding their effect on error resilience.
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
George Mason University（乔治梅森大学）；Dhaka University of Engineering & Technology（达卡工程技术大学）；Universite Polytechnique Hauts-De-France（上法兰西理工大学）；CNRS（法国国家科学研究中心）；IEMN（微电子与纳米技术研究所）
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
SAMOVAR, Télécom SudParis（SAMOVAR，法国电信南巴黎学院）；Department of Computer Science, German University of Technology in Oman (GUtech)（阿曼技术大学计算机科学系）；L3i, La Rochelle University（拉罗什大学L3i）
REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration
Humboldt University of Berlin（柏林洪堡大学）；Tallinn University of Technology（塔林理工大学）；University of Zanjan（赞詹大学）；Brandenburg University of Technology Cottbus-Senftenberg（勃兰登堡工业大学科特布斯-森夫滕贝格分校）
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
University of Cambridge（剑桥大学）；University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Southern California（南加利福尼亚大学）；Amazon（亚马逊）；Army Research Laboratory（陆军研究实验室）
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Paul G. Allen School of Computer Science & Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；AWS（亚马逊云科技）；Shanghai Jiao Tong University（上海交通大学）；UC Davis（加州大学戴维斯分校）；Cornell（康奈尔大学）
