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Extropic introduces Z1T, a sparse transformer-like model optimized for their probabilistic Z1 hardware to enhance energy efficiency in transformer inference through algorithmic-hardware co-design.
This paper presents a scalable backpropagation-based algorithm for training deep convolutional networks to run on thermodynamic Ising hardware, achieving 94.9% on CIFAR-10 and 76.0% on CIFAR-100 while analyzing inference cost-accuracy tradeoffs.
Thermocompute is a PyTorch emulator for thermodynamic probabilistic computing that enables neural network layers to achieve constant modeled physical time inference by exploiting parallel thermodynamic substrate, with immediate GPU-usable stochastic layers.