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Found 11 papers, 1 papers with code
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.
All-to-all reconfigurability with sparse Ising machines: the XORSAT challenge with p-bits
The 3R3X problem has a glassy energy landscape and it has recently been used to benchmark various IMs and other solvers.
Machine Learning Quantum Systems with Magnetic p-bits
The slowing down of Moore's Law has led to a crisis as the computing workloads of Artificial Intelligence (AI) algorithms continue skyrocketing.
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.
Physics-inspired Ising Computing with Ring Oscillator Activated p-bits
Our results highlight the promise of massively scaled p-computers with millions of free-running p-bits made out of nanoscale building blocks such as stochastic magnetic tunnel junctions.
Double Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits
One such approach is to use a low barrier nanomagnet as the free layer of a magnetic tunnel junction (MTJ) whose magnetic fluctuations are converted to resistance fluctuations in the presence of a stable fixed layer.
Mesoscale and Nanoscale Physics Emerging Technologies
The promise of spintronics for unconventional computing
Novel computational paradigms may provide the blueprint to help solving the time and energy limitations that we face with our modern computers, and provide solutions to complex problems more efficiently (with reduced time, power consumption and/or less device footprint) than is currently possible with standard approaches.
Applied Physics Mesoscale and Nanoscale Physics
Composable Probabilistic Inference Networks Using MRAM-based Stochastic Neurons
Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restricted Boltzmann machines (RBMs) to form deep belief networks (DBNs).
Reservoir Computing using Stochastic p-Bits
We present a general hardware framework for building networks that directly implement Reservoir Computing, a popular software method for implementing and training Recurrent Neural Networks and are particularly suited for temporal inferencing and pattern recognition.
