Brewing the first ever automatic memory management utility for SpiNNaker: Real-Time Garbage Collection for STDP simulations

Mantas Mikaitis, David Lester

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

First generation SpiNNaker chip uses ARM968, with highly limited internal memory space, as its core element. In simulations of learning algorithms, many biologically plausible learning rules require history traces of each neuron’s activity to be stored. As a result, the history traces of neurons rapidly fill the internal memory space eventually reaching the limits of ARM968. To lower the possibility of memory overflow, we propose to introduce a memory management routine working in the background, which must respect the biological timing constraints of the SpiNNaker simulations. Real-time garbage collection is an automatic memory management technique that can satisfy these requirements. This study presents the first ever implementation of real-time garbage collector for SpiNNaker architecture and evaluates the performance, carefully considering the biological real-time constraints of the system.
Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks (IJCNN)
Pages3008-3015
ISBN (Electronic)978-1-5090-6183-9
DOIs
Publication statusPublished - 3 Jul 2017
EventInternational Joint Conference on Neural Networks (IJCNN 2017) - United States, Alaska, United States
Duration: 14 May 201719 May 2017

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN 2017)
Country/TerritoryUnited States
CityAlaska
Period14/05/1719/05/17

Keywords

  • garbage collection
  • automatic memory management
  • hard real-time systems

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