Simulating Building Blocks for Spikes Signals Processing

Jiménez-Fernández, Ángel; Domínguez-Morales, Manuel; Cerezuela-Escudero, Elena; Paz-Vicente, R.; Linares-Barranco, A.; Jiménez, G.

doi:10.1007/978-3-642-21498-1_69

Cited by 7 publications

(11 citation statements)

References 14 publications

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“…The main difficulty of this processing system is finding a mechanism capable of performing the subtraction operation between two spiking signals. Regarding this problem, Jiménez-Fernández [21] provided the basic building blocks to operate with spiking signals. In that study, some basic components implemented in Hardware Description Language (HDL) were developed, which form the so-called "spiking algebra".…”

Section: Global Disparity Calculationmentioning

confidence: 99%

Neuronal Specialization for Fine-Grained Distance Estimation Using a Real-Time Bio-Inspired Stereo Vision System

Domínguez-Morales

Ríos-Navarro

et al. 2019

Electronics

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The human binocular system performs very complex operations in real-time tasks thanks to neuronal specialization and several specialized processing layers. For a classic computer vision system, being able to perform the same operation requires high computational costs that, in many cases, causes it to not work in real time: this is the case regarding distance estimation. This work details the functionality of the biological processing system, as well as the neuromorphic engineering research branch—the main purpose of which is to mimic neuronal processing. A distance estimation system based on the calculation of the binocular disparities with specialized neuron populations is developed. This system is characterized by several tests and executed in a real-time environment. The response of the system proves the similarity between it and human binocular processing. Further, the results show that the implemented system can work in a real-time environment, with a distance estimation error of 15% (8% for the characterization tests).

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Section: Global Disparity Calculationmentioning

confidence: 99%

Neuronal Specialization for Fine-Grained Distance Estimation Using a Real-Time Bio-Inspired Stereo Vision System

Domínguez-Morales

Ríos-Navarro

et al. 2019

Electronics

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“…These elements have been written in VHDL, which can be synthesized as digital circuits for FPGAs, and they can be used as building blocks for larger systems. Although in this paper we present a particular spike-based PID controller, designed building blocks allow to reuse and combine them in different ways, giving us the opportunity to build a diverse kind of systems for Spikes Signal Processing (SSP), like for example spike-based filters [17]. Spike processing building blocks are composed of dedicated hardware that works independently from each other, and thus, when synthesizing several of them on the same FPGA, they can behave as a parallel processing system.…”

Section: Spike-based Pid Controller Design Overviewmentioning

confidence: 99%

“…This component is used in the spike-based PID controller as an integral component, and we call it Spikes Integrate & Generate (SI&G) [17] (orange in Figure 2). SI&G is composed of a spike counter and an RB-SSG, as shown in Figure 10.…”

Section: Spike-based Pid Controller Design Overviewmentioning

confidence: 99%

“…Similarly the SH&F behavior can be extended to any kind of input spikes (U−, Y+ and Y- states) using the same logic: hold, cancel, and fire spikes according to the input spike ports and sign. This block has been successfully used previously to design spike-based processing systems (SSP) like several kinds of spike-based filters [ 17 , 22 , 23 ].…”

Section: Spike-based Pid Controller Design Overviewmentioning

confidence: 99%

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A Neuro-Inspired Spike-Based PID Motor Controller for Multi-Motor Robots with Low Cost FPGAs

Jiménez-Fernández

Jiménez-Moreno

Linares-Barranco

et al. 2012

Sensors

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In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

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“…In order to achieve the described goal, we have transformed the algorithm using existing spike processing blocks developed for our research group [ 21 , 25 , 26 ] and put them into MATLAB Simulink to test and adjust the blocks. Afterwards, two FPGA based boards were used to allocate the blocks and mimic the biological structure (one for the brain and the other one for the spinal cord).…”

Section: Introductionmentioning

confidence: 99%

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

Perez-Peña

Morgado-Estevez

Linares-Barranco

et al. 2013

Sensors

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In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation.

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Simulating Building Blocks for Spikes Signals Processing

Cited by 7 publications

References 14 publications

Neuronal Specialization for Fine-Grained Distance Estimation Using a Real-Time Bio-Inspired Stereo Vision System

Neuronal Specialization for Fine-Grained Distance Estimation Using a Real-Time Bio-Inspired Stereo Vision System

A Neuro-Inspired Spike-Based PID Motor Controller for Multi-Motor Robots with Low Cost FPGAs

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

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