  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.
