Layer-by-layer Adaptively Optimized ECC of NAND flash-based SSD Storing Convolutional Neural Network Weight for Scene Recognition

摘要

Layer-by-layer Adaptively Optimized Error Correcting Code (ECC) is proposed to improve the reliability of triple-level cell (TLC) NAND flash-based SSD for the scene recognition using convolutional neural network (CNN) of IoT edge devices. Layer-by-layer Adaptively Optimized ECC is composed of Layer-by-layer Iteration-Optimized Low Density Parity-Check (LBL-LDPC) and Layer-by-layer Code-length Adjusted Asymmetric Coding (LBL-AC). The conventional techniques like LDPC ECC and Asymmetric Coding (AC) improve the reliability. However, they require large overheads of the ECC decoding time and the flag/parity cell. Proposed LBL-LDPC and LBL-AC decrease the ECC decoding time by 14% and the data overhead by 26%, respectively, without recognition accuracy degradation. In addition, the data-retention time extends by 230%.