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 cm3, making it a portable, high-resolution, real-time imaging solution for personalized healthcare.

PoolFormer is a subset of Transformer neural network with a key difference of replacing computationally demanding token mixer with pooling function. In this work, a memristor-based PoolFormer network modeling and training framework for edge-artificial intelligence (AI) applications is presented. The original PoolFormer structure is further optimized for hardware implementation on RRAM crossbar by replacing the normalization operation with scaling. In addition, the nonidealities of RRAM crossbar from device to array level as well as peripheral readout circuits are analyzed. By integrating these factors into one training framework, the overall neural network performance is evaluated holistically and the impact of nonidealities to the network performance can be effectively mitigated. Implemented in Python and PyTorch, a 16-block PoolFormer network is built with$64\times 64$four-level RRAM crossbar array model extracted from measurement results. The total number of the proposed Edge PoolFormer network parameters is 0.246 M, which is at least one order smaller than the conventional CNN implementation. This network achieved inference accuracy of 88.07% for CIFAR-10 image classification tasks with accuracy degradation of 1.5% compared to the ideal software model with FP32 precision weights.

PoolFormer is a type of neural network architecture that is abstracted from Transformer where the computationally heavy token mixer module is replaced with simple pooling function. This paper presents a memristor-based PoolFormer modeling and training framework for edge-AI applications. To fit for implementation on resistive crossbar array, original PoolFormer structure is further optimized by replacing normalization operation with hardware friendly scaling operation. In addition, the non-idealities of RRAM crossbar from device to array level as well as peripheral readout circuits are also included. By incorporating these elements under a single framework for network training, their impact to the network performance can be effectively mitigated. This framework is implemented in a combination of Python and PyTorch. A 16-block PoolFormer network is designed and optimized for CIFAR-10 image classification tasks using measured$\mathbf{64}\times \mathbf{64}$RRAM crossbar array results. The total network weights are only 0.26M, which is at least one order of magnitude smaller than that of the conventional DNN implementation. When compared to the ideal model with FP64 weight bit-length, 85.86% inference accuracy is reached with only 4-level weight resolutions and less than 4% accuracy loss.

A novel convolution computing paradigm based on the NOR Flash Array is proposed. Significant improvements both in computing speed and energy consumption are achieved compared to CMOS-based logic computing paradigms. Regarding to the feature extraction task from a 256×256 image, the computing speed of 3.9×104frame per second (fps) and the energy consumption of 0.057nJ/pixel are achieved using the proposed computing paradigm.