211 citations · 425 across the 12 of their papers we have counts for
4 papers · 1 filter
Deep counter networks for asynchronous event-based processing
Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer
Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same…
Training Deep Spiking Neural Networks using Backpropagation
Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer
Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation. However, training…
Precise neural network computation with imprecise analog devices
Jonathan Binas, Daniel Neil, Giacomo Indiveri +2
The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency. Neve…
Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers
Jonathan Binas, Giacomo Indiveri, Michael Pfeiffer
Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task. Recently, it has been proposed that efficient stochastic solvers can be obtained thro…