A. Renner

F. Sheldon

A. Zlotnik

L. Tao

A. Sornborger

The training algorithm for digital neural networks is adapted and implemented entirely on an experimental chip inspired by brain physiology.

Spiking neural networks, designed to mimic biological brains, could be faster and more efficient than their digital counterparts but traditional training methods have been challenging to implement on neuromorphic hardware that supports them. Here, we demonstrate ‘on-chip’ learning by successfully executing the backpropagation algorithm, which computes weights between nodes, on Intel’s Loihi neuromorphic processor.

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.

The backpropagation algorithm implemented on spiking neuromorphic hardware