Nonvolatile Binary CNN Accelerator with Extremely Low Standby Power using RRAM for IoT Applications Recently, with the development of 5G communications technology, a fully interconnected world is coming. Because 5G has the characteristics of low power consumption, high speed, low cost and small delay, the change brought to the Internet of Things industry is dramatic 1. Artificial intelligence technology has great potential in the field of IoT devices, but the huge computational complexity makes it difficult to be realized on a powercritical device. In this paper, we demonstrate a nonvolatile binary convolutional neural network accelerator. The main contributions of this work are summarized as follows: (1) A nonvolatile binary CNN data path based on RRAM, which can be fully powergated in standby state; (2) The matrix multiplication and addition is performed by RRAM other than digital logic, with the binary weights stored in the RRAM; (3) Since the accelerator can be fully powered down, the power dissipated during the standby state is almost zero.

PowerAware FPGA Mapping of Convolutional Neural Networks With an unprecedented accuracy in numerous AI tasks, convolutional neural networks (CNNs) are rapidly deployed on powerlimited mobile and embedded applications. Existing mapping approaches focus on achieving high performance without explicit consideration of power consumption, leading to suboptimal solutions when power is considered in a subsequent stage. In this context, there is an emerging need for poweraware methodologies for the design of custom CNN engines. In this work, a methodology is presented for modelling the power consumption of FPGAbased CNN accelerators using a highlevel description of modules, together with a powercentric search strategy for exploring powerperformance tradeoffs within the CNNtoFPGA design space. By integrating into an existing CNNtoFPGA toolflow, the proposed power estimation method can yield a prediction accuracy of 93.4% for total system power consumption. Furthermore, it is demonstrated that the associated poweroriented exploration approach can generate CNN accelerators with a 20.1% power reduction over a purely throughputdriven design for AlexNet, maintaining the designu0027s throughput.