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Neurally-inspired Computing

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If you have a question about this talk, please contact Emli-Mari Nel .

This work-in-progress talk is about performing useful computation in a manner that is robust to both the failure of hardware and the re-allocation of hardware to other tasks. The computation is carried out with many identical computational sub-units. Each sub-unit is able to perform some relatively simple computations and has a single bit of storage associated to it.

We deal with three tasks: representing a set, creating an error-correcting code and an associative memory. All solutions have the property that at any point one can take computational sub-units away from the task at hand and the probability of error will increase gracefully. For the error-correcting code and the associative memory one can add sub-units to a task, without changing any of the other units and after a short time they will be used in the task and lower the rate of bit errors.

This talk is part of the Inference Group series.

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