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Mapping of local and global synapses on spiking neuromorphic hardware
Conference proceeding   Open access

Mapping of local and global synapses on spiking neuromorphic hardware

Anup Das, Yuefeng Wu, Khanh Huynh, Francesco Dell'Anna, Francky Catthoor, Siebren Schaafsma and IEEE
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), v 2018-, pp 1217-1222
Mar 2018
DOI: https://doi.org/10.23919/DATE.2018.8342201
url
http://arxiv.org/abs/1908.08024View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Computer architecture Distortion Hardware Neuromorphics Neurons Particle swarm optimization Synapses
Spiking Neural Networks (SNNs) are widely deployed to solve complex pattern recognition, function approximation and image classification tasks. With the growing size and complexity of these networks, hardware implementation becomes challenging because scaling up the size of a single array (crossbar) of fully connected neurons is no longer feasible due to strict energy budget. Modern neromorphic hardware integrates small-sized crossbars with time-multiplexed interconnects. Partitioning SNNs becomes essential in order to map them on neuromorphic hardware with the major aim to reduce the global communication latency and energy overhead. To achieve this goal, we propose our instantiation of particle swarm optimization, which partitions SNNs into local synapses (mapped on crossbars) and global synapses (mapped on time-multiplexed interconnects), with the objective of reducing spike communication on the interconnect. This improves latency, power consumption as well as application performance by reducing inter-spike interval distortion and spike disorders. Our framework is implemented in Python, interfacing CARLsim, a GPU-accelerated application-level spiking neural network simulator with an extended version of Noxim, for simulating time-multiplexed interconnects. Experiments are conducted with realistic and synthetic SNN-based applications with different computation models, topologies and spike coding schemes. Using power numbers from in-house neuromorphic chips, we demonstrate significant reductions in energy consumption and spike latency over PACMAN, the widely-used partitioning technique for SNNs on SpiNNaker.

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47 citations in Web of Science
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Automation & Control Systems
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
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