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arXiv · 2608.03033

Interpolation of Non-Linear Functions for LLMs using Partial Reconfiguration in FPGAs

Roger Morales-Monge·Nazareth Jimenez-Chacon·Jose Gabriel Villalobos-Alvarado·Luis G. Leon-Vega·Jorge Castro-Godinez
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Abstract

Non-linear functions such as exponential and sigmoid are essential in AI and LLM acceleration, although implementing them efficiently on FPGAs is still costly. This paper proposes a PWL interpolation framework based on partial reconfiguration to reduce hardware cost while preserving flexibility. The architecture separates the design into a static region for communication and control, and a reconfigurable region where different interpolation modules can be dynamically loaded. Uniform and non-uniform segmentation strategies are evaluated for exponential and sigmoid functions using FP16 and FP32 arithmetic. Results show that non-uniform segmentation can improve accuracy in high-curvature regions, while uniform segmentation offers lower hardware overhead. At the system level, the reconfigurable implementation achieved significant area savings, reaching up to 43\% less LUTs, 50\% less flip-flops, BRAMs and DSPs cells, compared against a static design containing both operators; all this with predictable reconfiguration latency. These results show that partial reconfiguration is a practical approach for exploring area-latency trade-offs in FPGA-based acceleration of non-linear functions for LLM workloads.

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Roger Morales-Monge, Nazareth Jimenez-Chacon, Jose Gabriel Villalobos-Alvarado, Luis G. Leon-Vega, Jorge Castro-Godinez. 2026-08-04. Interpolation of Non-Linear Functions for LLMs using Partial Reconfiguration in FPGAs. https://arxiv.org/abs/2608.03033

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