Abstract
As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In this work, we explore the feasibility of connecting these two models by implementing a generalized SI model on SNN. In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing. The coupled neurons communicate and modulate each other's action potentials through event-driven spikes and synchronize their dynamics around the states of optimal solutions. We demonstrate that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions. Furthermore, we demonstrate an efficient implementation of such neural dynamics on an emerging hardware platform, namely ferroelectric field-effect transistor (FeFET) based spiking neurons. Such an emerging in-silico neuron is composed of a compact 1T-1FeFET structure with both excitatory and inhibitory inputs. We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.
Highlights
Recent advances of deep learning models have initiated a resurgence of neural networks in the field of artificial intelligence (LeCun et al, 2015)
We explore the use of ferroelectric field-effect transistor (FeFET) based spiking neurons in the design of the proposed Swarm Intelligence (SI)-Spiking Neural Network (SNN) architecture
The innovation of Izhikevich’s model is to use a slow variable to control the leak current of a Leaky Integrate-and-Fire (LIF) model. Inspired from such a design, we propose to take advantage of inhibitory input VGF in FeFET spiking neuron to imitate the function of the “slow variable” because the FeFET is responsible for the “resetting” phase of a spike (Fang et al, 2019)
Summary
Recent advances of deep learning models have initiated a resurgence of neural networks in the field of artificial intelligence (LeCun et al, 2015). Of particular interest are optimization problems including NP-hard problem, such as constraint satisfaction problems (CSP) (Mostafa et al, 2015; Fonseca Guerra and Furber, 2017), vortex coloring problems (Parihar et al, 2017) and traveling salesman problems (TSP) (Jonke et al, 2016). These neural-inspired computing systems are designed exclusively so that the system converges at problem solutions by harvesting both deterministic as well as stochastic dynamics. Solving CSP with SNN is promising, it is enticing to note that the computational platform that we empirically find in the human brain can solve complex optimization problems
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