The Photoacoustic Quality-Enhancement Neural Network Processor with the Scalable and End-to-End Architecture by Improving the Sparsity Level

Recent advancements have marked significant progress in photoacoustic imaging as an effective method for acquiring deep bio-tissue visuals in modern medical clinical therapy and the efficacy of U-Net and its variants has been established for imaging quality enhancement in this field. Unlike common computer vision datasets such as ImageNet [1] and PASCAL VOC [2], biomedical images exhibit highly structured patterns, low spatial resolution, and single-channel modality, as shown in Fig. 1. Additionally, the U-Net parameters trained for medical super-resolution tasks demonstrate a high sparsity ratio, making them suitable for implementation on edge-computing platforms. Therefore, developing an energy-efficient photoacoustic imaging setup in this area is a natural progression. However, this development is constrained by the current neural network architectures, which are built around a U-Net backbone. The multi-stage feature extractor, skip connection integration across different blocks, and the encoder-decoder backbone design pose significant challenges to cutting-edge computational hardware platforms. In this study, a scalable, sparsity-supported neural network accelerator architecture for bio-tissue imaging quality enhancement is proposed to meet the stringent requirements of latency and energy efficiency, as depicted in Fig. 2. This architecture achieves desired performance improvements by exploring the sparsity possibilities in neural network during the training process and implementing an end-to-end pixel-first hardware design to minimize data movement and support sparsity computation. Compared with the state-of-the-art related works, this optimized architecture has achieved minimum on-chip storage overhead and the fastest frame for the application of photoacoustic imaging quality enhancement. The scalable architecture has also been implemented on a Xilinx XCZU9EG FPGA and attains a performance of PSNR@ 24 dB and a frame rate of 164 fps at a working frequency of 250 MHz.