Exploration of the viability of TiN/TiOX ReRAM in Computational Random-Access Memory (CRAM)

In-memory computing is a promising solution for solving the von-Neumann bottleneck. In particular, computational random-access memory (CRAM) is a promising form of in-memory computing where cascading logic operations can be performed directly within the memory array. However, a recent experiment uti...

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Bibliographic Details
Published in2024 IEEE International Conference on Rebooting Computing (ICRC) pp. 1 - 9
Main Authors Zink, Brandon R., Madhavan, Advait, Borders, William A., McClelland, Jabez
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.12.2024
Subjects
Accuracy
Adders
Computational Random-Access Memory
In-memory computing
Logic
Logic arrays
Magnetoresistive devices
Monte Carlo methods
Performance evaluation
Resistance
Resistive Random-Access Memory
Switches
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DOI10.1109/ICRC64395.2024.10937018

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Summary:In-memory computing is a promising solution for solving the von-Neumann bottleneck. In particular, computational random-access memory (CRAM) is a promising form of in-memory computing where cascading logic operations can be performed directly within the memory array. However, a recent experiment utilizing magnetoresistve devices as the memory element in CRAM only gave the correct answer in 63 % of trials. One way to improve the accuracy is to build CRAM cells using resistive devices with larger ON/OFF ratios. In this study, we explore the performance of CRAM using resistive random-access memory (ReRAM) cells. Using experimental data obtained from various TiN/TiO X -based ReRAM devices in Monte Carlo simulations, we determine that the performance of the full adder operation using ReRAM based CRAM is still subject to the same inaccuracies as CRAM that utilizes magnetoresistive devices. However, our analysis reveals that by reducing the write voltages and removing the effects of complementary resistive switching in the ReRAM devices, 100 % accuracy over 100,000 trials can be achieved.
DOI:10.1109/ICRC64395.2024.10937018