A Reconfigurable 4T2R ReRAM Computing In-Memory Macro for Efficient Edge Applications

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 o...

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Bibliographic Details
Published in:IEEE open journal of circuits and systems 2021, Vol.2, p.210-222
Main Authors: Chen, Yuzong, Lu, Lu, Kim, Bongjin, Kim, Tony Tae-Hyoung
Format: Article
Language:English
Subjects:
Associative memory
CMOS
Computation
Computer architecture
computing in-memory (CIM)
Datasets
Delays
Energy efficiency
Monte Carlo methods
Monte Carlo simulation
Neural networks
Nonvolatile memory
Random access memory
reconfigurable architecture
Reconfiguration
Resistive random access memory (ReRAM)
ternary content addressable memory (TCAM)
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Summary: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\times128 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.
ISSN:2644-1225
2644-1225
DOI:10.1109/OJCAS.2020.3042550