A Probabilistic Self-Annealing Compute Fabric Based on 560 Hexagonally Coupled Ring Oscillators for Solving Combinatorial Optimization Problems

Ahmed, Ibrahim; Chiu, Po-Wei; Kim, Chris H.

doi:10.1109/vlsicircuits18222.2020.9162869

Cited by 36 publications

(11 citation statements)

References 2 publications

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“…The p-computer can then provide N p f c samples per second, N p being the number of parallel units 8 , and f c the clock frequency. We argue that even with N p = 1, this throughput is well in excess of what is achieved with standard implementations on either CPU or GPU for a broad range of applications and algorithms including but not limited to those targeted by modern digital annealers or Ising solvers [9][10][11][12][13][14][15][16][17] . Interestingly, a p-computer also provides a conceptual bridge to quantum computing, sharing many characteristics that we associate with the latter.…”

Section: Introductionmentioning

confidence: 92%

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Probabilistic computing with p-bits

Kaiser,

Datta

2021

Preprint

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Digital computers store information in the form of bits that can take on one of two values 0 and 1, while quantum computers are based on qubits that are described by a complex wavefunction whose squared magnitude gives the probability of measuring either a 0 or a 1. Here we make the case for a probabilistic computer based on p-bits which take on values 0 and 1 with controlled probabilities and can be implemented with specialized compact energy-efficient hardware. We propose a generic architecture for such p-computers and show that they can significantly accelerate randomized algorithms used in a wide variety of applications including but not limited to Bayesian networks, optimization, Ising models and quantum Monte Carlo.

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Section: Introductionmentioning

confidence: 92%

“…In principle, the energy function is arbitrary, but much of the work is based on quadratic energy functions defined by a connection matrix W ij and a bias vector h i (see for example [9][10][11][12][13][14][15][16][17] ):…”

Section: Ising Modelmentioning

confidence: 99%

Probabilistic computing with p-bits

Kaiser,

Datta

2021

Preprint

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“…Given the importance of optimization problems, a lot of research has gone into developing algorithms and identifying appropriate hardware for Ising computing. Various approaches including quantum computers based on quantum annealing (QA) or adiabatic quantum optimization (AQC) implemented with superconducting circuits 11 , coherent Ising machines (CIMs) implemented with laser pulses 12 , phasechange oscillators 13 , or CMOS oscillators [14][15][16][17] and digital annealers based on simulated annealing (SA) 18 implemented with digital circuits 1,[19][20][21][22][23][24] are being explored.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation of Hardware Binary Stochastic Neurons

Hassan

Datta

2021

Phys. Rev. Applied

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Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a ground-state search of the Ising model. A common theme of such Ising Machines is to tailor the physics of underlying hardware to the mathematics of the Ising model to improve some aspect of performance that is measured in speed to solution, energy consumption per solution or area footprint of the adopted hardware. One such approach to build an Ising spin, or a binary stochastic neuron (BSN), is a compact mixed-signal unit based on a low-barrier nanomagnet based design that uses a single magnetic tunnel junction (MTJ) and three transistors (3T-1MTJ) where the MTJ functions as a stochastic resistor (1SR). Such a compact unit can drastically reduce the area footprint of BSNs while promising massive scalability by leveraging the existing Magnetic RAM (MRAM) technology that has integrated 1T-1MTJ cells in ∼ Gbit densities. The 3T-1SR design however can be realized using different materials or devices that provide naturally fluctuating resistances. Extending previous work, we evaluate hardware BSNs from this general perspective by classifying necessary and sufficient conditions to design a fast and energy-efficient BSN that can be used in scaled Ising Machine implementations. We connect our device analysis to systems-level metrics by emphasizing hardware-independent figures-of-merit such as flips per second and dissipated energy per random bit that can be used to classify any Ising Machine.

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“…There have been a number of approaches to build specialpurpose hardware to solve computationally hard problems. A class of such solvers (also known as Ising Machines) specifically solve quadratic energy models or the Ising model, typically mapped to problems in NP [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25],…”

Section: Introductionmentioning

confidence: 99%

Massively Parallel Probabilistic Computing with Sparse Ising Machines

Aadit,

Grimaldi,

Carpentieri

et al. 2021

Preprint

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A Probabilistic Self-Annealing Compute Fabric Based on 560 Hexagonally Coupled Ring Oscillators for Solving Combinatorial Optimization Problems

Cited by 36 publications

References 2 publications

Probabilistic computing with p-bits

Probabilistic computing with p-bits

Quantitative Evaluation of Hardware Binary Stochastic Neurons

Massively Parallel Probabilistic Computing with Sparse Ising Machines

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