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Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration
Mengyuan Yin, Benjamin Chen Ming Choong, Chuping Qu, Rick Siow Mong Goh, Weng-Fai Wong, Tao Luo
https://arxiv.org/abs/2508.17069

Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration
Learned activation functions in models like Kolmogorov-Arnold Networks (KANs) outperform fixed-activation architectures in terms of accuracy and interpretability; however, their computational complexity poses critical challenges for energy-constrained edge AI deployments. Conventional CPUs/GPUs incur prohibitive latency and power costs when evaluating higher order activations, limiting deployability under ultra-tight energy budgets. We address this via a reconfigurable lookup architecture with …