[HTML][HTML] Spintronic devices as next-generation computation accelerators
The ever increasing demand for computational power combined with the predicted plateau
for the miniaturization of existing silicon-based technologies has made the search for low …
for the miniaturization of existing silicon-based technologies has made the search for low …
Photonic probabilistic machine learning using quantum vacuum noise
Probabilistic machine learning utilizes controllable sources of randomness to encode
uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum …
uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum …
CMOS plus stochastic nanomagnets enabling 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. One …
transistors with emerging nanotechnologies (X) has become increasingly important. One …
Training deep Boltzmann networks with sparse Ising machines
The increasing use of domain-specific computing hardware and architectures has led to an
increasing demand for unconventional computing approaches. One such approach is the …
increasing demand for unconventional computing approaches. One such approach is the …
Leveraging volatile memristors in neuromorphic computing: from materials to system implementation
Inspired by the functions of biological neural networks, volatile memristors are essential for
implementing neuromorphic computing. These devices enable large-scale and energy …
implementing neuromorphic computing. These devices enable large-scale and energy …
[HTML][HTML] Quantum-noise-limited optical neural networks operating at a few quanta per activation
A practical limit to energy efficiency in computation is ultimately from noise, with quantum
noise [1] as the fundamental floor. Analog physical neural networks [2], which hold promise …
noise [1] as the fundamental floor. Analog physical neural networks [2], which hold promise …
Thermodynamic AI and the fluctuation frontier
Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic
fluctuations. We connect these physics-inspired AI algorithms by unifying them under a …
fluctuations. We connect these physics-inspired AI algorithms by unifying them under a …
Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions
Probabilistic (p-) computing is a physics-based approach to addressing computational
problems which are difficult to solve by conventional von Neumann computers. A key …
problems which are difficult to solve by conventional von Neumann computers. A key …
All-to-all reconfigurability with sparse and higher-order Ising machines
Abstract Domain-specific hardware to solve computationally hard optimization problems has
generated tremendous excitement. Here, we evaluate probabilistic bit (p-bit) based Ising …
generated tremendous excitement. Here, we evaluate probabilistic bit (p-bit) based Ising …
Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire
Y Wang, B Chen, W Gao, B Ye, C Niu… - National Science …, 2024 - academic.oup.com
Probabilistic computing has emerged as a viable approach to treat optimization problems.
To achieve superior computing performance, the key aspect during computation is massive …
To achieve superior computing performance, the key aspect during computation is massive …