Quadratic Unconstrained Binary Optimization (QUBO) on neuromorphic computing system

Alom, Zahangir; Essen, Brian Van; Moody, Adam; Widemann, David P.; Taha, Tarek M.

doi:10.1109/ijcnn.2017.7966350

Cited by 23 publications

(10 citation statements)

References 14 publications

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“…When implemented on neuromorphic architectures, these algorithms promise speed and efficiency gains by exploiting fine-grain parallelism and event-based computation. Examples include computational primitives, such as sorting, max, min, and median operations [70], a wide range of graph algorithms [71]- [74], NP-complete/hard problems, such as constraint satisfaction [75], boolean satisfiability [76], dynamic programming [77], and quadratic unconstrained binary optimization [78], [79], and novel Turing-complete computational frameworks, such as Stick [80] and SN P [81].…”

Section: C O M P U T I N G W I T H T I M Ementioning

confidence: 99%

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

et al. 2021

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Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today's neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based

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Section: C O M P U T I N G W I T H T I M Ementioning

confidence: 99%

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

et al. 2021

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“…Neuromorphic computing has also been used to find approximate solutions to NP-complete problems: several studies have shown that neuromorphic systems can achieve a similar performance in terms of time-to-solution and solution accuracy when compared with other conventional approaches, which use CPUs and GPUs to approximately solve NP-complete problems. For instance, Alom and co-workers used the IBM TrueNorth Neurosynaptic system to approximately solve the quadratic unconstrained binary optimization (QUBO) problem 96 . Mniszewski 97 converted the NP-complete graph partitioning problem to the QUBO problem and used the IBM TrueNorth system to solve it approximately: in some cases, neuromorphic solutions were more accurate than the solutions returned by the D-Wave quantum computer.…”

Section: Box 1 | Spiking Neural Networkmentioning

confidence: 99%

Opportunities for neuromorphic computing algorithms and applications

et al. 2022

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Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. Here, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.

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“…QUBO is an NP-hard problem, used to solve many applications and suitable to algorithms implementation in quantum annealing (Alom et al 2017;Tran et al 2016). Adiabatic quantum computers can aid to solve a QUBO problem, using the quantum mechanical process called quantum annealing, therefore being able to solve any NPhard problem like graph coloring (Date et al 2019b).…”

Section: Quadratic Unconstrained Binary Optimizationmentioning

confidence: 99%