Benchmarking Spiking Neurons for Linear Quadratic Regulator Control of Multi-linked Pole on a Cart: from Single Neuron to Ensemble

TL;DR

This paper explores the use of spiking neuron populations for LQR control of multi-linked cartpole systems, demonstrating improved control accuracy and resource efficiency on neuromorphic hardware.

Contribution

It introduces a scalable approach from single neurons to ensembles for neuromorphic control of complex systems, validated through simulations and hardware implementations.

Findings

Population encoding reduces neuron count needed for accurate control.

Neuromorphic implementation on Loihi achieves comparable control performance.

Linear control demonstrated on multi-linked pendula using neuron populations.

Abstract

The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking neurons, to reduce network complexity and optimize the number of neurons per core and hence, the chip size, in an application-specific neuromorphic hardware. While rate-encoding for spiking neurons provides a robust way to encode signals with the same number of neurons as an ANN, it often lacks precision. To achieve the desired accuracy, a population of neurons is often needed to encode the complete range of input signals. However, using population encoding immensely increases the total number of neurons required for a particular application, thus increasing the power consumption and on-board resource utilization. A transition from two neurons to a population of neurons for the LQR control of a cartpole is shown in this work. The near-linear behavior of a Leaky-Integrate-and-Fire neuron can be exploited to achieve the Linear Quadratic Regulator (LQR) control of a cartpole system. This has been shown in simulation, followed by a demonstration on a single-neuron hardware, known as Lu.i. The improvement in control performance is then demonstrated by using a population of varying numbers of neurons for similar control in the Nengo Neural Engineering Framework, on CPU and on Intel's Loihi neuromorphic chip. Finally, linear control is demonstrated for four multi-linked pendula on cart systems, using a population of neurons in Nengo, followed by an implementation of the same on Loihi. This study compares LQR control in the NEF using $7$ control and $7$ neuromorphic performance metrics, followed by a comparison with other conventional spiking and non-spiking controllers.