An MPSoC-based on-line Edge Infrastructure for Embedded Neuromorphic Robotic Controllers

TL;DR

This paper presents an upgraded neuromorphic control system for a robotic arm using an MPSoC, significantly reducing latency and power consumption by replacing traditional FPGA and computer hardware with a Xilinx Zynq-7000 SoC.

Contribution

The work introduces an all-in-one edge infrastructure for neuromorphic robotic controllers, enhancing response time and efficiency over previous FPGA-based systems.

Findings

Reduced latency in robotic control response

Lower power consumption of the control system

Simplified hardware architecture

Abstract

In this work, an all-in-one neuromorphic controller system with reduced latency and power consumption for a robotic arm is presented. Biological muscle movement consists of stretching and shrinking fibres via spike-commanded signals that come from motor neurons, which in turn are connected to a central pattern generator neural structure. In addition, biological systems are able to respond to diverse stimuli rather fast and efficiently, and this is based on the way information is coded within neural processes. As opposed to human-created encoding systems, neural ones use neurons and spikes to process the information and make weighted decisions based on a continuous learning process. The Event-Driven Scorbot platform (ED-Scorbot) consists of a 6 Degrees of Freedom (DoF) robotic arm whose controller implements a Spiking Proportional-Integrative- Derivative algorithm, mimicking in this way the previously commented biological systems. In this paper, we present an infrastructure upgrade to the ED-Scorbot platform, replacing the controller hardware, which was comprised of two Spartan Field Programmable Gate Arrays (FPGAs) and a barebone computer, with an edge device, the Xilinx Zynq-7000 SoC (System on Chip) which reduces the response time, power consumption and overall complexity.