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Training deep Boltzmann networks with sparse Ising machines

• belter 2y ago
  "...Using hardware-aware network topologies on field-programmable gate arrays, we train the full Modified National Institute of Standards and Technology (MNIST) and Fashion MNIST datasets without downsampling, as well as a reduced version of the Canadian Institute for Advanced Research, 10 classes (CIFAR-10) dataset. For the MNIST dataset, our machine, which has 4,264 nodes (p-bits) and about 30,000 parameters, can achieve the same classification accuracy (90%) as an optimized software-based restricted Boltzmann machine with approximately 3.25 million parameters.Similar results are achieved for the Fashion MNIST and CIFAR-10 datasets. The sparse deep Boltzmann network can also generate new handwritten digits and fashion products, a task the software-based restricted Boltzmann machine fails at..."