This paper presents a field-programmable gate array (FPGA) based medical image processing framework using a hardware-software co-design approach for biomedical tasks such as Malaria and Pneumonia detection. The design is implemented on the AMD-Xilinx UltraScale+ MPSoC (ZCU104) FPGA, focusing on optimizing data movement between the Processing System (PS) and Programmable Logic (PL) through a customized high-level synthesis (HLS) process. Depth-wise convolution is employed to reduce computational complexity, while layer fusion is applied to optimize layer-wise execution, and custom cache is integrated to improve memory access efficiency. The accelerated architecture is integrated with AXI interconnects and tested using the PYNQ overlay process. The experimental results demonstrate that the proposed accelerator achieves a throughput of 298.22 FPS and 205.87 FPS for the detection of malaria and pneumonia, respectively. The proposed design significantly improves energy efficiency, consuming 14.62 mJ/img for the detection of malaria and 23.89 mJ/img for the detection of pneumonia. Compared to alternative hardware platforms like Raspberry Pi with Coral TPU, the FPGA-based implementation offers superior performance, achieving 8.3× higher throughput and 4.3× better energy efficiency, making it well-suited for real-time medical image processing applications.
Yadav et al. (Wed,) studied this question.