Variation-Tolerant and Low R-Ratio Compute-in-Memory ReRAM Macro with Capacitive Ternary MAC Operation

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Abstract

A novel Resistive random access memory (ReRAM)-based Compute-in-memory (CIM) macro is proposed to overcome the limited accuracy and throughput of a conventional ReRAM-based CIM macro that results from to the low R-Ratio and large variation of ReRAM. The proposed structure consists of 1T2R1C bit-cells and 4-kb ReRAM-based nvCIM architecture with ternary weight and ternary input. Ternary multiplication is implemented with voltage division between paired ReRAM devices within a bit-cell to make the output voltage variation tolerant and less sensitive to low R-ratios. An accumulation operation is realized with capacitive coupling so that linearity can be guaranteed for a large number of operands, allowing accurate and fast multiply-and-accumulate (MAC) operations. For comprehensive validation of the proposed CIM macro, the Verilog-A models for ReRAM devices with an adjustable R-ratio and adjustable variations are adopted to perform simulation on various R-ratio and variation conditions. With the peripheral circuits designed in 180-nm CMOS technology, the proposed CIM macro is confirmed to have high variation tolerance, high throughput, and less sensitivity to a low R-ratio, resulting in a high ternary DNN accuracy of 99.07% (0.01% drop) for the MNIST and 83.79% (0.38% drop) for the CIFAR-10 data sets with an R-ratio as low as 38 and 20%/40% low/high resistance variation.

Original languageEnglish
Pages (from-to)2845-2856
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume69
Issue number7
DOIs
StatePublished - 1 Jul 2022
Externally publishedYes

Keywords

  • capacitive-coupling
  • Computing-in-memory (CIM)
  • deep neural network (DNN)
  • nonvolatile memory (NVM)
  • resistive RAM (ReRAM)
  • voltage division

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