基于超低功耗柔性精确场效应晶体管的内存模拟计算

T. Soliman, F. Müller, T. Kirchner, T. Hoffmann, H. Ganem, E. Karimov, T. Ali, M. Lederer, C. Sudarshan, T. Kämpfe, A. Guntoro, N. Wehn
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引用次数: 39

摘要

本文提出了一种基于铁电场效应管(FeFET)技术的高效交叉棒设计和实现,用于模拟内存计算(ACiM)加速的人工神经网络。本工作中提出的新型混合信号块减少了器件之间的差异,并针对低面积、低功耗和高吞吐量进行了优化。此外,我们还说明了采用位分解技术进行MAC操作的十字棒的操作和可编程性。我们基于交叉棒的ACiM加速器达到了13714 TOPS/W的创纪录峰值性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultra-Low Power Flexible Precision FeFET Based Analog In-Memory Computing
This paper presents an efficient crossbar design and implementation intended for analog compute-in-memory (ACiM) acceleration of artificial neural networks based on ferroelectric FET (FeFET) technology. The novel mixed signal blocks presented in this work reduce the device-to-device variation and are optimized for low area, low power and high throughput. In addition, we illustrate the operation and programmability of the crossbar that adopts bit decomposition techniques for MAC operation. Our crossbar based ACiM accelerator achieves a record peak performance of 13714 TOPS/W.
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