Physical Analog Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units
This work addresses the challenge of efficiently implementing the learnable nonlinear edge functions in Kolmogorov–Arnold Networks (KANs) in hardware by proposing an analog KAN architecture based on a Reconfigurable Nonlinear Processing Unit (RNPU). Leveraging multi-terminal nanosilicon devices that natively support programmable nonlinear transformations, the design employs the RNPU as its fundamental computational element, integrated with analog mixed-signal interfaces to achieve high parameter efficiency, low power consumption, and minimal silicon area for edge neural network deployment. Experimental results demonstrate that, at comparable approximation error levels, the proposed architecture reduces energy consumption by two to three orders of magnitude and chip area by approximately one order of magnitude relative to digital fixed-point MLP implementations, achieving a single-inference energy cost of merely 250 pJ with a latency of about 600 ns.