A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE

TL;DR

This paper presents a lightweight, FPGA-implemented Tiny Transformer model using Neural ODEs and quantization, achieving high accuracy and significant speedup for edge image recognition tasks.

Contribution

It introduces a novel hybrid model with Neural ODE backbone, optimized for FPGA deployment, reducing resource use and maintaining accuracy.

Findings

Achieves 79.68% top-1 accuracy on STL10 dataset.

Accelerates inference by 34.01× for backbone and MHSA components.

Provides 7.10× energy efficiency improvement over ARM Cortex-A53.

Abstract

Transformer has been adopted to image recognition tasks and shown to outperform CNNs and RNNs while it suffers from high training cost and computational complexity. To address these issues, a hybrid approach has become a recent research trend, which replaces a part of ResNet with an MHSA (Multi-Head Self-Attention). In this paper, we propose a lightweight hybrid model which uses Neural ODE (Ordinary Differential Equation) as a backbone instead of ResNet so that we can increase the number of iterations of building blocks while reusing the same parameters, mitigating the increase in parameter size per iteration. The proposed model is deployed on a modest-sized FPGA device for edge computing. The model is further quantized by QAT (Quantization Aware Training) scheme to reduce FPGA resource utilization while suppressing the accuracy loss. The quantized model achieves 79.68% top-1 accuracy for STL10 dataset that contains 96$\times$96 pixel images. The weights of the feature extraction network are stored on-chip to minimize the memory transfer overhead, allowing faster inference. By eliminating the overhead of memory transfers, inference can be executed seamlessly, leading to accelerated inference. The proposed FPGA implementation accelerates the backbone and MHSA parts by 34.01$\times$, and achieves an overall 9.85$\times$ speedup when taking into account the software pre- and post-processing. The FPGA acceleration leads to 7.10$\times$ better energy efficiency compared to the ARM Cortex-A53 CPU. The proposed lightweight Transformer model is demonstrated on Xilinx ZCU104 board for the image recognition of 96$\times$96 pixel images in this paper and can be applied to different image sizes by modifying the pre-processing layer.