# Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

@article{FonsecaGuerra2017UsingSS, title={Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems}, author={Gabriel A. Fonseca Guerra and Stephen B. Furber}, journal={Frontiers in Neuroscience}, year={2017}, volume={11} }

Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles…

## 38 Citations

Solving Constraint Satisfaction Problems Using the Loihi Spiking Neuromorphic Processor

- Computer Science2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)
- 2020

The work in this paper exhibits the first implementation of constraint satisfaction on a low power embedded neuromorphic processor, and aims to show that embedded spiking neuromorphic hardware is capable of executing general problem solving algorithms with great areal and computational efficiency.

Leveraging the Manycore Architecture of the Loihi Spiking Processor to Perform Quasi-Complete Constraint Satisfaction

- Computer Science2020 International Joint Conference on Neural Networks (IJCNN)
- 2020

This work exhibits the first implementation of constraint satisfaction on a low power embedded neuromorphic processor capable of generating a solution set, and shows that embedded spiking neuromorphic hardware is capable parallelizing the constraint satisfaction problem solving process to yield extreme gains in terms of time, power, and energy.

Solving Vertex Cover via Ising Model on a Neuromorphic Processor

- Computer Science2018 IEEE International Symposium on Circuits and Systems (ISCAS)
- 2018

This work demonstrates how a neuromorphic processor can be used to solve the classic vertex cover problem via an Ising spin model and states that space and time efficiency is decreased only by a constant factor without degrading solution quality.

A Neuromorphic Computational Primitive for Robust Context-Dependent Decision Making and Context-Dependent Stochastic Computation

- Computer ScienceIEEE Transactions on Circuits and Systems II: Express Briefs
- 2019

This work presents a mixed-signal analog/digital neuromorphic implementation of a state-dependent SNN architecture that relies on synaptic dis-inhibition to ensure robust decision making even in the face of very large variability in constraint satisfaction problems (CSPs).

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

- Computer ScienceFront. Neurosci.
- 2019

This work explores the feasibility of connecting Swarm Intelligence and SNN by implementing a generalized SI model on SNN, and demonstrates that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions.

PrxCa1−xMnO3 based stochastic neuron for Boltzmann machine to solve “maximum cut” problem

- Computer ScienceAPL Materials
- 2019

The Analog Approximate Sigmoid (AAS) stochastic neuron is proposed to solve the maximum cut—an NP hard problem and the AAS design solves the problem with 98% accuracy, which is comparable with the DPS design but with 10× area and 4× energy advantage.

Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks

- Computer ScienceFront. Neurosci.
- 2019

This work presents a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where it is used for generative and discriminative computations on visual data and implicitly demonstrates its robustness to various substrate-specific distortive effects.

Neuromorphic scaling advantages for energy-efficient random walk computations

- Computer ScienceNature Electronics
- 2022

Despite being in an early development stage, it is found that NMC platforms, at a sufficient scale, can drastically reduce the energy demands of high-performance computing (HPC) platforms.

A Spiking Recurrent Neural Network With Phase-Change Memory Neurons and Synapses for the Accelerated Solution of Constraint Satisfaction Problems

- Computer ScienceIEEE Journal on Exploratory Solid-State Computational Devices and Circuits
- 2020

A stochastic spiking neuron based on a phase-change memory (PCM) device for the solution of CSPs within a Hopfield recurrent neural network (RNN).

Neuromorphic scaling advantages for energy-efficient random walk computation

- Computer ScienceArXiv
- 2021

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