| Autor: |
Yuzong Chen, Lu Lu, Bongjin Kim, Tony Tae-Hyoung Kim |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Open Journal of Circuits and Systems, Vol 2, Pp 210-222 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2644-1225 |
| DOI: |
10.1109/OJCAS.2020.3042550 |
| Popis: |
Resistive random access memory (ReRAM)-based computing in-memory (CIM) is a promising solution to overcome the von-Neumann bottleneck in conventional computing architectures. We propose a reconfigurable ReRAM architecture using a novel 4T2R bit-cell that supports non-volatile storage and two types of CIM operations: i) ternary content addressable memory (TCAM) and ii) in-memory dot product (IM-DP) for neural networks. The proposed 4T2R cell occupies a smaller area than prior SRAM-based CIM bit-cells. A 128 × 128 ReRAM macro is designed in 40nm CMOS technology. For TCAM operations, it allows a search word-length of 128 bits. For IM-DP operations, it can compute parallel dot products using binary inputs and ternary weights. The simulated search delay for TCAM operation is 0.92 ns at VDD = 0.9 V and the simulated energy efficiency for IM-DP operation is 223.6 TOPS/W at VDD = 0.7 V. Monte-Carlo simulations show a standard deviation of 4.9% in accumulate operation for IM-DP which corresponds to a classification accuracy of 95.7% on the MNIST dataset and 81.7% on the CIFAR-10 dataset. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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