Enhancing the Accuracy of Resistive In-Memory Architectures using Adaptive Signal Processing

Analog in-memory computing architectures (IMCs) have exhibited high energy efficiency over conventional digital architectures. The use of resistive memory arrays such as magnetic RAM (MRAM) in IMCs has significant potential due to their high-density and non-volatility. However, the analog nature of...

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Bibliographic Details
Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5
Main Authors Ou, Han-Mo, Shanbhag, Naresh R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
Adaptation models
Adaptive signal processing
Computational modeling
Computer architecture
digital signal processing
error compensation
In-memory computing
in-memory computing architectures
Magnetoacoustic effects
MRAM
nonvolatile memory
Random access memory
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Summary:Analog in-memory computing architectures (IMCs) have exhibited high energy efficiency over conventional digital architectures. The use of resistive memory arrays such as magnetic RAM (MRAM) in IMCs has significant potential due to their high-density and non-volatility. However, the analog nature of computation limits the compute accuracy of IMCs. We present activation scaling compensation (ASC), an adaptive signal processing method to compensate for the effects of bitline (BL) and sourceline (SL) parasitic resistances in MRAM-based IMC matrix-vector multiplication (MVM) architectures. Through behavioral modeling and simulation, we demonstrate that ASC enhances the compute signal-to-noise ratio (SNR) by 8.6dB-to-13.9dB with negligible energy overhead. The effectiveness of ASC-enabled MRAM-based IMC is demonstrated in the context of a signal recovery problem.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096954