Energy efficiency enhancement has become an increasingly important issue for battery electric vehicles. Even if it can be improved in many ways, the driver’s driving pattern strongly influences the battery energy consumption of a vehicle. In this paper, eco assist techniques to simply implement an energy-efficient driving assistant system are introduced, including eco guide, eco control and eco monitoring methods. The eco guide is provided to control the vehicle speed and accelerator pedal stroke, and eco control is suggested to limit the output power of the battery. For eco monitoring, the eco indicator and eco report are suggested to teach eco-friendly driving habits. The vehicle test, which is done in four ways, consists of federal test procedure (FTP)-75, new european driving cycle (NEDC), city and highway cycles, and visual feedback with audible warnings is provided to attract the driver’s voluntary participation. The vehicle test result shows that the energy usage efficiency can be increased up to 19.41%.

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Neural-network computing has revolutionized the field of machine learning. The systolicarray architecture is a widely used architecture for neural-network computing acceleration that was adopted by Google in its Tensor Processing Unit (TPU). To ensure the correct operation of the neural network, the reliability of the systolic-array architecture should be guaranteed. This paper proposes an efficient systolic-array redundancy architecture that is based on systolic-array partitioning and rearranging connections of the systolic-array elements. The proposed architecture allows both offline and online repair with an extended redundancy architecture and programmable fuses and can ensure reliability even in an online situation, for which the previous fault-tolerant schemes have not been considered.

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