Accelerating compute intensive non-real-time beam-forming algorithms in ultrasound imaging using deep learning architectures has been gaining momentum in the recent past. Nonetheless, the complexity of the state-of-the-art deep learning techniques poses challenges for deployment on resource-constrained edge devices. In this work, we propose a novel vision transformer based tiny beamformer (Tiny-VBF), which works on the raw radio-frequency channel data acquired through single-angle plane wave insonification. The output of our Tiny-VBF provides fast envelope detection requiring very low frame rate, i.e. 0.34 GOPs/Frame for a frame size of 368 x 128 in comparison to the state-of-the-art deep learning models. It also exhibited an 8% increase in contrast and gains of 5% and 33% in axial and lateral resolution respectively when compared to Tiny-CNN on in-vitro dataset. Additionally, our model showed a 4.2% increase in contrast and gains of 4% and 20% in axial and lateral resolution respectively when compared against conventional Delay-and-Sum (DAS) beamformer. We further propose an accelerator architecture and implement our Tiny-VBF model on a Zynq UltraScale+ MPSoC ZCU104 FPGA using a hybrid quantization scheme with 50% less resource consumption compared to the floating-point implementation, while preserving the image quality.

翻译：近年来，利用深度学习架构加速超声成像中计算密集的非实时波束形成算法已成为研究热点。然而，当前最先进的深度学习技术因其复杂性在资源受限的边缘设备部署中面临挑战。本文提出一种基于视觉Transformer的新型微型波束形成器（Tiny-VBF），该波束形成器直接处理通过单角度平面波激励获取的原始射频通道数据。与现有深度学习模型相比，Tiny-VBF输出可实现快速包络检测，且帧率极低——以368×128图像尺寸为例，计算量仅为0.34 GOPs/帧。在体外数据集测试中，相较于微型卷积神经网络（Tiny-CNN），本模型对比度提升8%，轴向分辨率与横向分辨率分别提高5%和33%。与传统延迟叠加（DAS）波束形成器相比，其对比度提升4.2%，轴向与横向分辨率分别提升4%和20%。此外，我们提出一种加速器架构，采用混合量化方案在Zynq UltraScale+ MPSoC ZCU104 FPGA上实现Tiny-VBF模型，在保持图像质量的前提下，资源消耗较浮点实现减少50%。