Memory designs require timing margins to compensate for aging and fabrication process variations. With technology downscaling, aging mechanisms became more apparent, and larger margins are considered necessary. This, in return, means a larger area requirement and lower performance for the memory. Bias Temperature Instability (BTI) is one of the main contributors to aging, which slows down transistors and ultimately causes permanent faults. In this paper, first, we propose a low-cost aging mitigation scheme, which can be applied to existing hardware to mitigate aging on memory address decoder logic. We mitigate the BTI effect on critical transistors by applying a rejuvenation workload to the memory. Such an auxiliary workload is executed periodically to rejuvenate transistors that are located on critical paths of the address decoder. Second, we analyze workloads' efficiency to optimize the mitigation scheme. Experimental results performed with realistic benchmarks demonstrate several-times lifetime extension with a negligible execution overhead.

ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage.