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Synaptic Sampling of Neural Networks
Probabilistic artificial neural networks offer intriguing prospects for enabling the uncertainty of artificial intelligence methods to be described explicitly in their function; however, the development of techniques that quantify uncertainty by well-understood methods such as Monte Carlo sampling has been limited by the high costs of stochastic sampling on deterministic computing hardware.
Stochastic Neuromorphic Circuits for Solving MAXCUT
By designing circuits and algorithms that make use of randomness similarly to natural brains, we hypothesize that the intrinsic randomness in microelectronics devices could be turned into a valuable component of a neuromorphic architecture enabling more efficient computations.
Neuromorphic scaling advantages for energy-efficient random walk computation
Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities.
Solving a steady-state PDE using spiking networks and neuromorphic hardware
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations.
