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arXiv · 2603.19338

DAPA: Distribution Aware Piecewise Activation Functions for On-Device Transformer Inference and Training

Maoyang Xiang·Bo Wang
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Abstract

Non-linear activation functions play a pivotal role in on-device inference and training, as they not only consume substantial hardware resources but also impose a significant impact on system performance and energy efficiency. In this work, we propose Distribution-Aware Piecewise Activation (DAPA), a differentiable and hardware-friendly activation function for Transformer architectures by exploiting the distribution of pre-activation data. DAPA employs a non-uniform piecewise approximation that allocates finer segments to high-probability regions of the distribution, improving generalizability over prior piecewise linear methods. The resulting approximation is further quantized using Distribution-Weighted Mean Square Error to reduce latency and resource utilization for hardware deployment. Our HLS implementation demonstrates that DAPA speeds up GELU computation by 16$\times$ and decreases DSP utilization by 16$\times$ while maintaining comparable or better performance across vision Transformers and GPT-2 models.

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Maoyang Xiang, Bo Wang. 2026-03-19. DAPA: Distribution Aware Piecewise Activation Functions for On-Device Transformer Inference and Training. https://doi.org/10.1145/3770743.3804274

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