40nm, 2M-Cell, 8b精度,混合SLC-MLC PCM内存宏,20.5 - 65.0TOPS/W,用于微型边缘设备

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

摘要

高效的边缘计算,加上足够大的片上存储容量,在万物互联时代至关重要。非易失性内存计算(nvCIM)通过使计算更接近内存来减少数据传输开销[1]-[4]。多层电池(MLC)比单层电池(SLC)具有更高的存储密度。已经提出了一些MLC或模拟nvCIM设计,但它们要么针对更简单的神经网络模型[5],要么使用面积效率较低的差分单元[6]来实现。此外,使用一种存储类型表示整个权重向量不会利用上下位之间的巨大精度差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 40-nm, 2M-Cell, 8b-Precision, Hybrid SLC-MLC PCM Computing-in-Memory Macro with 20.5 - 65.0TOPS/W for Tiny-Al Edge Devices
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.
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