Weight Quantization in Spiking Neural Network for Hardware Implementation

Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic blocks of brain-like systems. Memristor-based neuromorphic hardware solutions for multi-agent systems are considered as challenges in frontier areas of chip design for fast and energy-efficient computing. As functional materials, metal oxide thin films with resistive switching and memory effects (memristive structures) are recognized as a potential elemental base for new components of neuromorphic engineering, enabling a combination of both data storage and processing in a single unit. A key design issue in this case is a hardware defined functionality of neural networks. The gradient change of resistive properties of memristive elements and its non-volatile memory behavior ensure the possibility of spiking neural network organization with unsupervised learning through hardware implementation of basic synaptic mechanisms, such as Hebb's learning rules including spike — timing dependent plasticity, long-term potentiation and depression. This paper provides an overview of scientific researches carrying out at Saint Petersburg Electrotechnical University "LETI" since 2014 in the field of novel electronic components for neuromorphic hardware solutions of brain-like chip design. Among the most promising concepts developed by ETU "LETI" are: the design of metal-insulator-metal structures exhibiting multilevel resistive switching (gradient tuning of resistive properties and bipolar resistive switching are combined together in a sin¬gle memristive element) for further use as artificial synaptic devices in neuromorphic chips; computing schemes for spatio-temporal pattern recognition based on spiking neural network architecture implementation; breadboard models of analogue circuits of hardware implementation of neuromorphic blocks for brain-like system developing.

Download Full-text

Conversion of Artificial Neural Network to Spiking Neural Network for Hardware Implementation

2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) ◽

10.1109/icce-tw46550.2019.8991758 ◽

2019 ◽

Author(s):

Yi-Lun Chen ◽

Chih-Cheng Lu ◽

Kai-Cheung Juang ◽

Kea-Tiong Tang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hardware Implementation ◽

Spiking Neural Network ◽

Artificial Neural

Download Full-text

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

Computational Intelligence and Neuroscience ◽

10.1155/2016/5615618 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 13

Author(s):

A. Espinal ◽

H. Rostro-Gonzalez ◽

M. Carpio ◽

E. I. Guerra-Hernandez ◽

M. Ornelas-Rodriguez ◽

...

Keyword(s):

Neural Network ◽

Hardware Implementation ◽

Pattern Generator ◽

Quadruped Robot ◽

Spiking Neural Network ◽

Resource Usage ◽

Hexapod Robot ◽

Biologically Inspired ◽

Robot Locomotion ◽

Locomotion System

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

Download Full-text