Practical Edge Kernels for Integer-Only Vision Transformers Under Post-training Quantization

In the domain of computer vision, transformer models have shown noteworthy success, prompting extensive research on optimizing their inference, particularly concerning their deployment on edge devices. While quantization has emerged as a viable solution for enabling energy efficiency in Convolutional Neural Networks (CNNs), achieving direct quantization of complex activation and normalization operators in transformer models proves to be a challenging task. Existing methods that rely on 64-bit integers often suffer from data truncation issues when deployed to energy-constrained edge devices, resulting in a significant loss of model accuracy. In this paper, we propose a range-constrained quantization technique for activation and normalization operators in transformers that addresses the dilemma between data range and precision. Our approach is the first 32-bit integer-based edge kernel implementation for vision transformers with post-training integer-only quantization, ensuring both efficiency and accuracy. Experimental results demonstrate a remarkable 5 times kernel speedup when deployed on two different ARM CPUs, with negligible accuracy loss in comparison to full-precision vision transformers. This innovative work is poised to significantly impact the deployment of transformer models on energy-efficient edge devices.