Ernesto Sanchez

Mohammad Hasan Ahmadilivani, Mario Barbareschi, Salvatore Barone et al.

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of hardware architectures running DNNs has to be evaluated, usually through costly fault injection campaigns. This paper explores the approximation and fault resiliency of DNN accelerators. We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based simulation framework. Further, we propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks

Franco Oberti, Alessandro Savino, Ernesto Sanchez et al.

The automobile industry is no longer relying on pure mechanical systems; instead, it benefits from advanced Electronic Control Units (ECUs) in order to provide new and complex functionalities in the effort to move toward fully connected cars. However, connected cars provide a dangerous playground for hackers. Vehicles are becoming increasingly vulnerable to cyber attacks as they come equipped with more connected features and control systems. This situation may expose strategic assets in the automotive value chain. In this scenario, the Controller Area Network (CAN) is the most widely used communication protocol in the automotive domain. However, this protocol lacks encryption and authentication. Consequently, any malicious/hijacked node can cause catastrophic accidents and financial loss. Starting from the analysis of the vulnerability connected to the CAN communication protocol in the automotive domain, this paper proposes EXT-TAURUM P2T a new low-cost secure CAN-FD architecture for the automotive domain implementing secure communication among ECUs, a novel key provisioning strategy, intelligent throughput management, and hardware signature mechanisms. The proposed architecture has been implemented, resorting to a commercial Multi-Protocol Vehicle Interface module, and the obtained results experimentally demonstrate the approach's feasibility.