A state-space definition of fading memory is introduced that extends incremental input-to-output stability via a memory kernel, is implied by incremental input-to-state stability under bounded inputs, and holds for current-driven memristor models.
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BMRUs enable analog recurrent neural network hardware via discrete outputs that suppress noise 20-fold, with one-to-one parameter-to-circuit mapping and linear power scaling for recurrence.
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State-space fading memory
A state-space definition of fading memory is introduced that extends incremental input-to-output stability via a memory kernel, is implied by incremental input-to-state stability under bounded inputs, and holds for current-driven memristor models.
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Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
BMRUs enable analog recurrent neural network hardware via discrete outputs that suppress noise 20-fold, with one-to-one parameter-to-circuit mapping and linear power scaling for recurrence.