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Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers
The xMFTs are used to estimate the averages and correlations during the positive phase of the contrastive divergence (CD) algorithm and our custom-designed p-computer is used to estimate the averages and correlations in the negative phase.
CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important.
Training Deep Boltzmann Networks with Sparse Ising Machines
These results demonstrate the potential of using Ising machines for traditionally hard-to-train deep generative Boltzmann networks, with further possible improvement in nanodevice-based realizations.
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
The transistor celebrated its 75${}^\text{th}$ birthday in 2022.
