Hardware annealing on DT-CNN using CAM&lt;sup&gt;2&lt;/sup&gt;

Fujita, Toshio; Sakomizu, Kazuhito; Ogura, Takeshi

doi:10.1109/cnna.2010.5430328

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Alternatively, one can consider a massively parallel system of simple computing elements that tackle different parts of the problem simultaneously, and discover a global solution through communicating local search mechanisms. Here, we explore event-based neural hardware as a substrate of computation, however, there are various other approaches, such as quantum annealing [1], special cellular automata hardware [2], or oscillator networks [3]. The idea of using recurrent neural networks for finding solutions to computationally hard problems goes back to Hinton [4] and Hopfield [5].…”

Section: Introductionmentioning

confidence: 99%

Spiking analog VLSI neuron assemblies as constraint satisfaction problem solvers

Binas

Indiveri

Pfeiffer

2016

2016 IEEE International Symposium on Circuits and Systems (ISCAS)

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Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task. Recently, it has been proposed that efficient stochastic solvers can be obtained through appropriately configured spiking neural networks performing Markov Chain Monte Carlo (MCMC) sampling. The possibility to run such models on massively parallel, low-power neuromorphic hardware holds great promise; however, previously proposed networks are based on probabilistically spiking neurons, and thus rely on random number generators or external noise sources to achieve the necessary stochasticity, leading to significant overhead in the implementation. Here we show how stochasticity can be achieved by implementing deterministic models of integrate and fire neurons using subthreshold analog circuits that are affected by thermal noise. We present an efficient implementation of spikebased CSP solvers using a reconfigurable neural network VLSI device, and the device's intrinsic noise as a source of randomness. To illustrate the overall concept, we implement a generic Sudoku solver based on our approach and demonstrate its operation. We establish a link between the neuron parameters and the system dynamics, allowing for a simple temperature control mechanism.

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