The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
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by
Alpha Renner, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, Andrew Sornborger
2021
Abstract
The capabilities of natural neural systems have inspired new generations of
machine learning algorithms as well as neuromorphic very large-scale integrated
(VLSI) circuits capable of fast, low-power information processing. However, it
has been argued that most modern machine learning algorithms are not
neurophysiologically plausible. In particular, the workhorse of modern deep
learning, the backpropagation algorithm, has proven difficult to translate to
neuromorphic hardware. In this study, we present a neuromorphic, spiking
backpropagation algorithm based on synfire-gated dynamical information
coordination and processing, implemented on Intel's Loihi neuromorphic research
processor. We demonstrate a proof-of-principle three-layer circuit that learns
to classify digits from the MNIST dataset. To our knowledge, this is the first
work to show a Spiking Neural Network (SNN) implementation of the
backpropagation algorithm that is fully on-chip, without a computer in the
loop. It is competitive in accuracy with off-chip trained SNNs and achieves an
energy-delay product suitable for edge computing. This implementation shows a
path for using in-memory, massively parallel neuromorphic processors for
low-power, low-latency implementation of modern deep learning applications.
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