Sun, P., Jiang, W., Chee, P. Y., & Botteldooren, D. (2024). Cell-Stitching for Analog Neuromorphic Computing. TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON), 571–574. https://doi.org/10.1109/tencon61640.2024.10903095
Abstract:
Neuromorphic computing is an innovative paradigm aiming
to unify storage and computation, thereby addressing the constraints
imposed by the Von Neumann bottleneck. Within this
domain, analog computing based on memristors as synaptic
elements has emerged as a promising avenue, though achieving
consistent accuracy has proven to be challenging, due to limited
precision of memristors. One promising strategy involves harnessing
multiple memristor cells to improve synaptic precision,
yet a mere concatenation approach as published in the literature
falls short of meeting this goal, as inherent variations in the
memristor writing process would render errors of higher order
bit cells to overshadow lower order bits cells. In response to
this challenge, we present a novel ’cell splicing’ methodology
designed to enhance accuracy with analog computation. Experimental
simulations using the ImageNet dataset demonstrate
that it achieves accuracy similar to the baseline, and markedly
outperforms the rudimentary concatenation approach.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - RIE2020 Advanced Manufacturing and Engineering (AME) Neuromorphic Computing Programme
Grant Reference no. : A1687b0033
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Manufacturing, Trade, and Connectivity Programmatic Grant - Van der Waals Engineering for All-optical Neuromorphic Chip
Grant Reference no. : M23M2b0056