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The paper presents a biologically grounded framework where noisy neural and synaptic dynamics enable probabilistic inference and learning via stochastic sampling, using analogue in-memory computing hardware for scalable and energy-efficient computation.
This paper introduces KANalogue, a fully analogue implementation of Kolmogorov-Arnold Networks that uses negative-differential-resistance devices to perform learnable nonlinear functions directly in hardware. It achieves competitive accuracy with fewer parameters than analogue MLPs on MNIST, FashionMNIST, and CIFAR-10.
BIN16 replaces all floating-point operations with boolean operations (XNOR+popcount) for neural network training and inference, enabling direct computation in off-the-shelf DRAM with zero floats, gradients, or hyperparameter tuning. It achieves 82% accuracy on MNIST in a single epoch, using only 220 lines of C.