Construction of a Spike-Based Memory Using Neural-Like Logic Gates Based on Spiking Neural Networks on SpiNNaker

Abstract

Neuromorphic engineering concentrates the efforts of a large number of researchers due to its great potential as a field of research, in a search for the exploitation of the advantages of the biological nervous system and the brain as a whole for the design of more efficient and real-time capable applications. For the development of applications as close to biology as possible, Spiking Neural Networks (SNNs) are used, which are considered biologically-plausible and constitute the third generation of Artificial Neural Networks. This work presents a spiking implementation of a memory, which is one of the most important components in computer architecture. In the process of designing this spiking memory, different intermediate components were also implemented and tested. The tests were carried out on the SpiNNaker neuromorphic platform. This work goes into the development of spiking blocks using a logic gate approach based on previous work and includes a comparison between other works in the state of the art related to spiking memories and the one proposed here. All the implemented blocks and developed tests are available in a public repository.