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Blockchain technology continues to grow and extend into more areas with great success, which highlights the importance of studying the fields that have been, and have yet to be, fundamentally changed by its entrance. In particular, blockchain technology has been shown to be increasingly relevant in the field of transportation systems. More studies continue to be conducted relating to both fields of study and their integration. It is anticipated that their existing relationships will be greatly improved in the near future, as more research is conducted and applications are better understood. Because blockchain technology is still relatively new as compared to older, more well-used methods, many of its future capabilities are still very much unknown. However, before they can be discovered, we need to fully understand past and current developments, as well as expert observations, in applying blockchain technology to the autonomous vehicle field. From an understanding and discussion of the current and potential future capabilities of blockchain technology, as provided through this survey, advancements can be made to create solutions to problems that are inherent in autonomous vehicle systems today. The focus of this paper is mainly on the potential applications of blockchain in the future of transportation systems to be integrated with connected and autonomous vehicles (CAVs) to provide a broad overview on the current related literature and research studies in this field. 

Magneto-Electric FET ( MEFET ) is a recently developed post-CMOS FET, which offers intriguing characteristics for high-speed and low-power design in both logic and memory applications. In this article, we present MeF-RAM , a non-volatile cache memory design based on 2-Transistor-1-MEFET ( 2T1M ) memory bit-cell with separate read and write paths. We show that with proper co-design across MEFET device, memory cell circuit, and array architecture, MeF-RAM is a promising candidate for fast non-volatile memory ( NVM ). To evaluate its cache performance in the memory system, we, for the first time, build a device-to-architecture cross-layer evaluation framework to quantitatively analyze and benchmark the MeF-RAM design with other memory technologies, including both volatile memory (i.e., SRAM, eDRAM) and other popular non-volatile emerging memory (i.e., ReRAM, STT-MRAM, and SOT-MRAM). The experiment results for the PARSEC benchmark suite indicate that, as an L2 cache memory, MeF-RAM reduces Energy Area Latency ( EAT ) product on average by ~98% and ~70% compared with typical 6T-SRAM and 2T1R SOT-MRAM counterparts, respectively.