1. A High-Efficiency Charge-Domain Compute-in-Memory 1F1C Macro Using 2-bit FeFET Cells for DNN Processing
- Author
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Nellie Laleni, Franz Muller, Gonzalo Cunarro, Thomas Kampfe, and Taekwang Jang
- Subjects
1FeFET-1Capacitance (1F1C) ,artificial intelligence (AI) accelerator ,charge domain computing ,compute-in-memory (CIM) ,ferroelectric field-effect transistor (FeFET) ,multilevel memory cells ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
This article introduces a 1FeFET-1Capacitance (1F1C) macro based on a 2-bit ferroelectric field-effect transistor (FeFET) cell operating in the charge domain, marking a significant advancement in nonvolatile memory (NVM) and compute-in-memory (CIM). Traditionally, NVMs, such as FeFETs or resistive RAMs (RRAMs), have operated in a single-bit fashion, limiting their computational density and throughput. In contrast, the proposed 2-bit FeFET cell enables higher storage density and improves the computational efficiency in CIM architectures. The macro achieves 111.6 TOPS/W, highlighting its energy efficiency, and demonstrates robust performance on the CIFAR-10 dataset, achieving 89% accuracy with a VGG-8 neural network. These findings underscore the potential of charge-domain, multilevel NVM cells in pushing the boundaries of artificial intelligence (AI) acceleration and energy-efficient computing.
- Published
- 2024
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