Abstract
This letter proposes C2FeRAM, a 2T2C/cell ferroelectric compute-in-memory (CiM) scheme for energy-efficient and high-reliability edge inference and transfer learning. With certain area overhead, C2FeRAM achieves the following highlights: (i) compared with FeFET/FeMFET, it achieves disturb-free CiM and much higher write endurance (equal to FeRAM), leading to 100× inference time with < 1% accuracy drop for VGG8 in CIFAR-10 dataset, along with the enhanced endurance for weight updates, e.g., CiM-based transfer learning; (ii) compared with 1T1C FeRAM inference cache, the achieved disturb-free feature and CiM capability in C2FeRAM lead to improvements of 4× energy, 200× speed, and 3.2e 5× life cycles. Such benefits highlight an intriguing solution for future intelligent edge AI.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1088-1091 |
| Number of pages | 4 |
| Journal | IEEE Electron Device Letters |
| Volume | 44 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 2023 |
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering
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