Title: Managing a real-time massively-parallel neural architecture
Author: Patterson, James Cameron
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2012
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EThOS Persistent ID: uk.bl.ethos.558047 
Supervisor: Garside, James; Furber, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Abstract:
A human brain has billions of processing elements operating simultaneously; the only practical way to model this computationally is with a massively-parallel computer. A computer on such a significant scale requires hundreds of thousands of interconnected processing elements, a complex environment which requires many levels of monitoring, management and control. Management begins from the moment power is applied and continues whilst the application software loads, executes, and the results are downloaded. This is the story of the research and development of a framework of scalable management tools that support SpiNNaker, a novel computing architecture designed to model spiking neural networks of biologically-significant sizes. This management framework provides solutions from the most fundamental set of power-on self-tests, through to complex, real-time monitoring of the health of the hardware and the software during simulation. The framework devised uses standard tools where appropriate, covering hardware up / down events and capacity information, through to bespoke software developed to provide real-time insight to neural network software operation across multiple levels of abstraction. With this layered management approach, users (or automated agents) have access to results dynamically and are able to make informed decisions on required actions in real-time.
Keywords: SpiNNaker ; neural network ; spiking neural network ; ANN ; SNN ; visualisation ; visualization ; embedded ; real-time ; management ; SNMP ; steering ; HPC ; parallel ; artificial neural network
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