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A Universal Abstract-Time Platform for Real-Time Neural Networks

A.D. Rast, M.M. Khan, X. Jin, L.A. Plana and S.B. Furber


High-speed asynchronous hardware makes it possible to virtualise neural networks' temporal dynamics as well as their structure. Through SpiNNaker, a dedicated neural chip multiprocessor, we introduce a real-time modelling architecture that makes the neural model run on the device independent of the hardware specifics. The central features of this modelling architecture are: native concurrency, ability to support very large (>> 10^9 neurons) networks, and decoupling of the temporal and spatial characteristics of the model from those of the hardware. It circumvents a virtually fatal tradeoff in large-scale neural hardware between model support limitations or scalability limitations, without imposing a synchronous timing model. The chip itself combines an array of general-purpose processors with a configurable asynchronous interconnect and memory fabric to achieve true on- and off-chip parallelism, universal network architecture support, and programmable temporal dynamics. An HDL-like concurrent configuration software model using libraries of templates, allows the user to embed the neural model onto the hardware, mapping the virtual network structure and time dynamics into physical on-chip components and delay specifications. Initial modelling experiments demonstrate the ability of the processor to support real-time neural processing using 2 different neural models. The complete system is therefore an environment able, within a wide range of model characteristics, to model real-time dynamic neural network behaviour on dedicated hardware.

PDF (619K). IEEE Copyright