用查表法FPGA加速可学习激活函数，能效提升超1万倍。
Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration
- 用可重构FPGA+自适应查表，减少复杂计算
- 相比边缘CPU/GPU，能效提升超10^4倍
- 适合需部署后动态适配的边缘AI场景
Kolmogorov-Arnold Networks（KANs）等模型中的可学习激活函数在准确率和可解释性上优于固定激活函数，但其计算复杂度给能量受限的边缘AI部署带来挑战。传统CPU/GPU在评估高阶激活时延迟和功耗过高，难以满足极低功耗需求。本文提出基于FPGA的可重构查表架构，结合细粒度量化与自适应查找表，在降低能耗密集型运算的同时保持激活函数保真度。FPGA的可重构性支持部署后动态硬件优化，契合边缘系统对后期适应性的需求。在KAN模型上的评估显示，该设计相较边缘CPU/GPU实现超10⁴倍能效提升，精度一致且资源开销极小。本方案为计算密集、功耗敏感的边缘AI应用提供了实用路径，使自适应激活网络成为可能。
原文摘要 · Abstract (English)
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.
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