--Deep neural networks (DNNs) enhance the accuracy and efficiency of reconstructing key parameters from time-resolved photon arrival signals recorded by single-photon detectors. However, the performance of conventional backpropagation-based DNNs is highly dependent on various parameters of the optical setup and biological samples under examination, necessitating frequent network retraining--either through transfer learning or from scratch. Newly collected data must also be stored and transferred to a high-performance GPU server for retraining, introducing latency and storage overhead. T o address these challenges, we propose an online training algorithm based on a One-Sided Jacobi rotation-based Online Sequential Extreme Learning Machine (OSOS-ELM). We fully exploit parallelism in executing OSOS-ELM on a heterogeneous FPGA with integrated ARM cores. Extensive evaluations of OSOS-ELM and OS-ELM demonstrate that both achieve comparable accuracy across different network dimensions (i.e., input, hidden, and output layers), while OSOS-ELM proves to be more hardware-efficient. By leveraging the parallelism of OSOS-ELM, we implement a holistic computing prototype on a Xilinx ZCU104 FPGA, which integrates a multi-core CPU and programmable logic fabric. We also implement our OSOS-ELM on the NVIDIA Jetson Xavier NX GPU to comprehensively investigate its computing performance on another type of heterogeneous computing platform. N-device training of neural networks has been emerging in recent decades. On-device training and inference save the overhead of data transfer to data centers, memory management, and computing on the cloud. The number of edge devices is increasing exponentially and is expected to reach 1 trillion by 2035 [1]. Latency tends to be a bottleneck of real-time applications such as healthcare and machine automation. Additionally, information privacy can be threatened when uploading and offloading sensitive biomedical data to the cloud. This work is supported by the EPSRC (EP/T00097X/1); the Quantum Technology Hub in Quantum Imaging (QuantiC), and the University of Strathclyde. Xingda Li also acknowledges support from China Scholarship Council.

Diffuse correlation spectroscopy (DCS) is an emerging noninvasive technique that measures the tissue blood flow, by using near-infrared coherent point-source illumination to detect spectral changes. While machine learning has demonstrated significant potential for measuring blood flow index (BFi), an open question concerning the success of this approach pertains to its robustness in scenarios involving deviations between datasets with varying Signal-to-Noise Ratios (SNRs) originating from diverse clinical applications and various setups. This study proposes a transfer learning approach, aims to assess the influence of SNRs on the generalization ability of learned features, and demonstrate the robustness for transfer learning. A synthetic dataset with varying levels of added noise is utilized to simulate different SNRs. The proposed network takes a 1x64 autocorrelation curve as input and generates BFi and the correlation parameter beta. The proposed model demonstrates excellent performance across different SNRs, exhibiting enhanced fitting accuracy, particularly for low SNR datasets when compared with other fitting methods. This highlights its potential for clinical diagnosis and treatment across various scenarios under different clinical setups.