Neuromorphic FeRAM-Based Co-Design for Imaging Enhancement in Handheld Photoacoustic Systems

This paper introduces a novel platform designed to enhance the imaging quality of handheld photoacoustic imaging (PAI) systems, addressing the limitations of current portable PAI devices. The platform integrates the MultiResU-Net imaging enhancement algorithm with a Ferroelectric random-access memory (FeRAM) crossbar array, enabling efficient in-memory computing that is highly suitable for deep neural networks involving extensive matrix multiplications. The hardware implementation is optimized for low-power operation on edge devices, and a specifically designed algorithmic strategy is introduced to accurately simulate hardware variations with a time complexity of O(mn). The feasibility and effectiveness of this approach are demonstrated through simulations using synthesized and in vivo data, showing a more than tenfold improvement in imaging resolution. The neural network inference is significantly accelerated, completing within microseconds, thereby fully supporting real-time imaging. The entire platform is compact, with dimensions of 25×25×20 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>, making it a portable, high-resolution, real-time imaging solution for personalized healthcare.