Demonstration of Differential Mode Ferroelectric Field-Effect Transistor Array-Based in-Memory Computing Macro for Realizing Multiprecision Mixed-Signal Artificial Intelligence Accelerator

Vivek Parmar, Franz Müller, Jing Hua Hsuen, Sandeep Kaur Kingra, Nellie Laleni, Yannick Raffel, Maximilian Lederer, Alptekin Vardar, Konrad Seidel, Taha Soliman, Tobias Kirchner, Tarek Ali, Stefan Dünkel, Sven Beyer, Tian Li Wu*, Sourav De*, Manan Suri*, Thomas Kämpfe

*此作品的通信作者

研究成果: Article同行評審

7 引文 斯高帕斯(Scopus)

摘要

Harnessing multibit precision in nonvolatile memory (NVM)-based synaptic core can accelerate multiply and accumulate (MAC) operation of deep neural network (DNN). However, NVM-based synaptic cores suffer from the trade-off between bit density and performance. The undesired performance degradation with scaling, limited bit precision, and asymmetry associated with weight update poses a severe bottleneck in realizing a high-density synaptic core. Herein, 1) evaluation of novel differential mode ferroelectric field-effect transistor (DM-FeFET) bitcell on a crossbar array of 4 K devices; 2) validation of weighted sum operation on 28 nm DM-FeFET crossbar array; 3) bit density of 223Mb mm−2, which is ≈2× improvement compared to conventional FeFET array; 4) 196 TOPS/W energy efficiency for VGG-8 network; and 5) superior bit error rate (BER) resilience showing ≈94% training and 88% inference accuracy with 1% BER are demonstrated.

原文English
文章編號2200389
期刊Advanced Intelligent Systems
5
發行號6
DOIs
出版狀態Published - 6月 2023

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