Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype.

Yexin Yan,Wolfgang Maass,Steve Furber,Robert Legenstein,Felix Neumarker,Sebastian Hoppner,Johannes Partzsch,Bernhard Vogginger,Christian Mayr,David Kappel

doi:10.1109/tbcas.2019.2906401

Abstract

Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions, such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the second generation SpiNNaker system is designed to overcome this problem. Low-power advanced RISC machine (ARM) processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations, the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local static random access memory (SRAM), leads to 62% energy reduction compared to the case without accelerators and the use of external dynamic random access memory (DRAM). The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this paper paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms.

Highlights

N EUROPHYSIOLOGICAL data suggest that brain networks are sparsely connected, highly dynamic and noisy [1], [2]
The reward-based synaptic sampling model can be scaled to future SpiNNaker 2 systems without major restrictions, i.e. as our implementation is integrated into the SpiNNaker software framework, the automatic mapping of larger networks onto many cores and the configuration of routing tables comes for free
A reward-based synaptic sampling model is implemented in the prototype chip of the second generation SpiNNaker system

Summary

Introduction

N EUROPHYSIOLOGICAL data suggest that brain networks are sparsely connected, highly dynamic and noisy [1], [2]. A single neuron is only connected to a fraction of potential postsynaptic partners and this sparse connectivity changes even in the adult brain on the timescale of hours to days [3], [4]. The dynamics that underlies the process of synaptic rewiring was found to be dominated by noise [5]. It has been further suggested that the permanently ongoing dynamics of synapses lead to a random walk that is well described by a stochastic driftdiffusion process, that gives rise to a stationary distribution over synaptic strengths. Synapses are permanently changing and randomly rewiring while the overall statistics of the connectivity remains stable [6]–[9]. Theoretical considerations suggest that the brain is not suppressing these noise sources since they can be exploited as a computational resource to drive exploration of parameter spaces, and several models have been proposed to capture this feature of brain circuits (see [10] and [11] for reviews)

Results

Discussion

Conclusion