  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.
