Analysis of FPGA and software approaches to simulate unconventional computer architectures

Ghasempour, Mohsen; Heathcote, Jonathan; Navaridas, Javier; Plana, Luis A.; Garside, Jim; Luján, Mikel

doi:10.1109/reconfig.2015.7393292

Cited by 1 publication

(1 citation statement)

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“…This way, different concepts can be evaluated with little effort without the need to create specialized hardware, offering great flexibility to the designer. It should be mentioned that other network simulators do exist, such as the one introduced in Ghasempour et al ( 2015 ). This particular simulator offers a higher level of detail simulating the SpiNNaker interconnect network, but offers less flexibility for other concepts in general.…”

Section: Introductionmentioning

confidence: 99%

Verification of a neuromorphic computing network simulator using experimental traffic data

et al. 2022

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Simulations are a powerful tool to explore the design space of hardware systems, offering the flexibility to analyze different designs by simply changing parameters within the simulator setup. A precondition for the effectiveness of this methodology is that the simulation results accurately represent the real system. In a previous study, we introduced a simulator specifically designed to estimate the network load and latency to be observed on the connections in neuromorphic computing (NC) systems. The simulator was shown to be especially valuable in the case of large scale heterogeneous neural networks (NNs). In this work, we compare the network load measured on a SpiNNaker board running a NN in different configurations reported in the literature to the results obtained with our simulator running the same configurations. The simulated network loads show minor differences from the values reported in the ascribed publication but fall within the margin of error, considering the generation of the test case NN based on statistics that introduced variations. Having shown that the network simulator provides representative results for this type of —biological plausible—heterogeneous NNs, it also paves the way to further use of the simulator for more complex network analyses.

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Section: Introductionmentioning

confidence: 99%