Muhammad Mukaram Khan

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— SpiNNaker is a novel chip – based on the ARM processor – which is designed to support large scale spiking neural networks simulations. In this paper we describe some of the features that permit SpiNNaker chips to be connected together to form scalable massively-parallel systems. Our eventual goal is to be able to simulate neural networks consisting of 10(More)
&THE SPINNAKER (SPIKING Neural Network Architecture) project at the University of Manchester aims at simulating a billion spiking neurons in real time. Fortunately, such an application is an ideal candidate for massive parallelism, and unlike some forms of parallel processing, it needn't maintain consistency in shared memories. Neural models running in such(More)
— Given the limited current understanding of the neural model of computation, hardware neural network archi-tectures that impose a specific relationship between physical connectivity and model topology are likely to be overly restrictive. Here we introduce, in the SpiNNaker chip, an alternative approach: a mappable virtual topology using an asynchronous(More)
Real-time modelling of large neural systems places critical demands on the processing system's dynamic model. With spiking neu-ral networks it is convenient to abstract each spike to a point event. In addition to the representational simplification, the event model confers the ability to defer state updates, if the model does not propagate the effects of(More)
Large-scale neural simulation virtually necessitates dedicated hardware with on-chip learning. Using SpiNNaker, a universal neural network chip multiprocessor, we demonstrate an STDP implementation as an example of programmable on-chip learning for dedicated neural hardware. By using a pre-synaptic sensitive scheme, we optimize both the data representation(More)
—This paper demonstrates the feasibility and evaluates the performance of using the SpiNNaker neuromorphic hardware to simulate traditional non-spiking multi-layer per-ceptron networks with the backpropagation learning rule. In addition to investigating the mapping of checker-boarding partitioning scheme onto SpiNNaker, we propose a new algorithm called(More)
Transaction processing over mobile networks faces new challenges due to limitations in bandwidth and available power, as well as due to intermittent connectivity that causes loss of data and transaction aborts. Besides, the possibility of security breach increases substantially due to the frequent motion of clients across cells, which gives rise to novel(More)
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