  This paper reported a bespoke adder-based deep learning network for
time-domain fluorescence lifetime imaging (FLIM). By leveraging the l1-norm
extraction method, we propose a 1-D Fluorescence Lifetime AdderNet (FLAN)
without multiplication-based convolutions to reduce the computational
complexity. Further, we compressed fluorescence decays in temporal dimension
using a log-scale merging technique to discard redundant temporal information
derived as log-scaling FLAN (FLAN+LS). FLAN+LS achieves 0.11 and 0.23
compression ratios compared with FLAN and a conventional 1-D convolutional
neural network (1-D CNN) while maintaining high accuracy in retrieving
lifetimes. We extensively evaluated FLAN and FLAN+LS using synthetic and real
data. A traditional fitting method and other non-fitting, high-accuracy
algorithms were compared with our networks for synthetic data. Our networks
attained a minor reconstruction error in different photon-count scenarios. For
real data, we used fluorescent beads' data acquired by a confocal microscope to
validate the effectiveness of real fluorophores, and our networks can
differentiate beads with different lifetimes. Additionally, we implemented the
network architecture on a field-programmable gate array (FPGA) with a
post-quantization technique to shorten the bit-width, thereby improving
computing efficiency. FLAN+LS on hardware achieves the highest computing
efficiency compared to 1-D CNN and FLAN. We also discussed the applicability of
our network and hardware architecture for other time-resolved biomedical
applications using photon-efficient, time-resolved sensors.
