W. Khwa, Yen-Cheng Chiu, Chuan-Jia Jhang, Sheng-Po Huang, Chun-Ying Lee, Tai-Hao Wen, Fu-Chun Chang, Shao-Ming Yu, T. Lee, M. Chang
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引用次数: 38
Abstract
Efficient edge computing, with sufficiently large on-chip memory capacity, is essential in the internet-of-everything era. Nonvolatile computing-in-memory (nvCIM) reduces the data transfer overhead by bringing computation closer, in proximity, to the memory [1]–[4]. While the multi-level cell (MLC) has higher storage density than the single-level cell (SLC). A few MLC or analog nvCIM designs had been proposed, but they either target simpler neural-net models [5] or are implemented using a less area-efficient differential cell [6]. Furthermore, representing the entire weight vector using one storage type does not exploit the drastic accuracy difference between the upper and the lower bits.