NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference

• URL: http://arxiv.org/abs/2112.02191v1
• Date: Fri, 3 Dec 2021 23:06:57 GMT
• Title: NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference
• Authors: Joonsang Yu, Junki Park, Seongmin Park, Minsoo Kim, Sihwa Lee, Dong Hyun Lee, Jungwook Choi
• Abstract summary: Non-linear operations such as GELU, Layer normalization, and Softmax are essential yet costly building blocks of Transformer models. This paper proposes an accurate and hardware-friendly approximation framework for efficient Transformer inference.
• Score: 9.329021390526124
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Non-linear operations such as GELU, Layer normalization, and Softmax are essential yet costly building blocks of Transformer models. Several prior works simplified these operations with look-up tables or integer computations, but such approximations suffer inferior accuracy or considerable hardware cost with long latency. This paper proposes an accurate and hardware-friendly approximation framework for efficient Transformer inference. Our framework employs a simple neural network as a universal approximator with its structure equivalently transformed into a LUT. The proposed framework called NN-LUT can accurately replace all the non-linear operations in popular BERT models with significant reductions in area, power consumption, and latency.

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