ArticleNature communications2015
An event-based architecture for solving constraint satisfaction problems.
Article in Nature communications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 62 citations in OpenAlex.
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- Photonic neuromorphic architecture for tens-of-task lifelong learning.Light, science & applications · 2024Article
- Using synchronized oscillators to compute the maximum independent set.Nature communications · 2020Article
- Memory devices and applications for in-memory computing.Nature nanotechnology · 2020Review
- Memristive and CMOS Devices for Neuromorphic Computing.Materials (Basel, Switzerland) · 2020Review
- A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision.Scientific reports · 2019Article
- A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- A continuous-time MaxSAT solver with high analog performance.Nature communications · 2018Article
- Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.Scientific reports · 2017Article
- Vertex coloring of graphs via phase dynamics of coupled oscillatory networks.Scientific reports · 2017Article
- Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems.Frontiers in neuroscience · 2017Article
- Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.Frontiers in neuroscience · 2016Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Constraint satisfaction problems are ubiquitous in many domains. They are typically solved using conventional digital computing architectures that do not reflect the distributed nature of many of these problems, and are thus ill-suited for solving them. Here we present a parallel analogue/digital hardware architecture specifically designed to solve such problems. We cast constraint satisfaction problems as networks of stereotyped nodes that communicate using digital pulses, or events. Each node contains an oscillator implemented using analogue circuits. The non-repeating phase relations among the oscillators drive the exploration of the solution space. We show that this hardware architecture can yield state-of-the-art performance on random SAT problems under reasonable assumptions on the implementation. We present measurements from a prototype electronic chip to demonstrate that a physical implementation of the proposed architecture is robust to practical non-idealities and to validate the theory proposed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.