Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration

TL;DR

This paper presents a reconfigurable FPGA-based lookup table approach for efficiently implementing learnable nonlinear activation functions in neural networks, significantly reducing energy consumption and latency for edge AI applications.

Contribution

It introduces a novel FPGA architecture with adaptive lookup tables and quantization to efficiently evaluate learned activation functions, enabling practical deployment on energy-constrained edge devices.

Findings

Achieves over 10,000x higher energy efficiency than CPUs and GPUs.

Maintains accuracy with minimal footprint overhead.

Demonstrates superior speed and energy savings in edge AI deployments.

Abstract

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 edge FPGAs. By coupling fine-grained quantization with adaptive lookup tables, our design minimizes energy-intensive arithmetic operations while preserving activation fidelity. FPGA reconfigurability enables dynamic hardware specialization for learned functions, a key advantage for edge systems that require post-deployment adaptability. Evaluations using KANs - where unique activation functions play a critical role - demonstrate that our FPGA-based design achieves superior computational speed and over $10^4$ times higher energy efficiency compared to edge CPUs and GPUs, while maintaining matching accuracy and minimal footprint overhead. This breakthrough positions our approach as a practical enabler for energy-critical edge AI, where computational intensity and power constraints traditionally preclude the use of adaptive activation networks.