IEEE Circuits and Systems Magazine - Q4 2022 - 26

Feature
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On the Accuracy of
Analog Neural Network
Inference Accelerators
t. Patrick Xiao, Member, IEEE, Ben feinberg, Member, IEEE, christopher h. Bennett, Member, IEEE,
Venkatraman Prabhakar, Prashant saxena, Vineet agrawal, sapan agarwal, Member, IEEE, and
matthew J. marinella, Senior Member, IEEE
Abstract
specialized accelerators have recently garnered attention as
a method to reduce the power consumption of neural network
inference. a promising category of accelerators utilizes nonvolatile
memory arrays to both store weights and perform in situ analog
computation inside the array. While prior work has explored
the design space of analog accelerators to optimize performance
and energy efficiency, there is seldom a rigorous evaluation
of the accuracy of these accelerators. this work shows
how architectural design decisions, particularly in mapping
neural network parameters to analog memory cells, influence
inference accuracy. When evaluated using resnet50 on Imagenet,
the resilience of the system to analog non-idealities-cell
Digital Object Identifier 10.1109/MCAS.2022.3214409
Date of current version: 13 January 2023
26
IEEE cIrcuIts and systEms magazInE
1531-636X/23©2023IEEE
fourth quartEr 2022
programming errors, analog-to-digital converter resolution, and
array parasitic resistances-all improve when analog quantities
in the hardware are made proportional to the numerical values
in the network. moreover, contrary to the assumptions of prior
work, nearly equivalent
resilience to cell
imprecision can be
achieved by fully storing weights as analog quantities, rather
than spreading weight bits across multiple devices, often referred
to as bit slicing. By exploiting proportionality, analog system
designers have the freedom to match the precision of the hardware
to the needs of the algorithm, rather than attempting to
guarantee the same level of precision in the intermediate results
as an equivalent digital accelerator. this ultimately results in an
analog accelerator that is more accurate, more robust to analog
errors, and more energy-efficient.

IEEE Circuits and Systems Magazine - Q4 2022

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