Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling

Aimone, James B.; Lehoucq, Richard B.; Severa, William; Smith, J. Darby

doi:10.1109/icrc53822.2021.00019

Cited by 4 publications

(3 citation statements)

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“…Samples drawn on neuromorphic can then be averaged according to (9) to obtain a solution to the PDE (8). Applications solved in this manner using neuromorphic samples include steady-state and time-dependent equations, and Boltzmann transport problems [85,86].…”

Section: Markov Chains and Pide Solutionsmentioning

confidence: 99%

“…Addressing whether or not the samples gathered from the density algorithm deployed on Loihi are statistically accurate [85], develops a methodology for comparing samples to the expected distribution by using measures of relative entropy as a hypothesis test. Using a common stochastic process as a test case, they find that even with the approximations needed in the algorithm and the hardware limited transition probabilities, that the samples generated sufficiently approximate the expected distribution.…”

Section: Neuromorphic Advantages and Remaining Challenges For Random ...mentioning

confidence: 99%

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A review of non-cognitive applications for neuromorphic computing

Aimone

Date

Guerra

et al. 2022

Neuromorph. Comput. Eng.

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Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (``cognitive'' applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

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Section: Markov Chains and Pide Solutionsmentioning

confidence: 99%

Section: Neuromorphic Advantages and Remaining Challenges For Random ...mentioning

confidence: 99%

A review of non-cognitive applications for neuromorphic computing

Aimone

Date

Guerra

et al. 2022

Neuromorph. Comput. Eng.

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“…[48,49] Likewise, neuron-level stochasticity, though using PRNGs as in Figure 3c, is what is available on today's large-scale spiking neuromorphic platforms and has been shown to be useful for numerical sampling applications on platforms including Intel's Loihi and IBM TrueNorth, and SpiN-Naker. [50][51][52] Nevertheless, the ability to effectively deploy stochasticity at the synapse memories itself (which the brain does), as opposed to just the neurons, likely will provide a more powerful probabilistic computing resource. Recently, a stochastic neural network was implemented with a crossbar array architecture with ferroelectric field effect transistor synapse weights connected to Ag/HfO 2 conducting bridge memory selector devices.…”

Section: Linking Probabilistic Computing To Neural Architecturesmentioning

confidence: 99%

Probabilistic Neural Computing with Stochastic Devices

et al. 2022

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The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.

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