5x可靠性增强的40nm TaOx Approximate-ReRAM与特定领域计算，用于物联网边缘设备的实时图像识别

2018 IEEE Symposium on VLSI Technology Pub Date : 2018-06-01 DOI:10.1109/VLSIT.2018.8510669

Y. Yamaga, Yoshiaki Deguchi, S. Fukuyama, K. Takeuchi

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引用次数: 3

摘要

提出了基于像素间数据匹配(P2P-DM)和像素间纠错码(IP-ECC)的高可靠近似reram (A-ReRAM)算法，通过深度神经网络(DNN)实现图像的准确识别。通过专为图像识别应用和基于像素间特征和ReRAM误差特征调制图像数据，ReRAM的数据保留时间和耐用性分别提高了5倍和3.3倍。

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5x Reliability Enhanced 40nm TaOx Approximate-ReRAM with Domain-Specific Computing for Real-time Image Recognition of IoT Edge Devices

Highly reliable Approximate-ReRAM (A-ReRAM) with Pixel-to-Pixel Data Matching (P2P-DM) and Inter-Pixel error-correcting code (IP-ECC) is proposed to recognize the image accurately by deep neural network (DNN). By specializing for the image recognition applications and modulating the image data based on pixel-to-pixel features and ReRAM error characteristics, data-retention time and endurance of ReRAM increases by 5x and 3.3x, respectively.

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2018 IEEE Symposium on VLSI Technology

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