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[Display omitted] • We propose a novel layer-centric event-routing architecture (LaCERA) and provide the mathematical background behind. • We thoroughly analyze LaCERA implemented in a field-programmable gate array (FPGA) in terms of memory use and event-routing latency. • To identify the efficacy and efficiency of LaCERA on the processor scale, we implement a neuromorphic processor with 32 cores (each of which utilizes LaCERA) in an FPGA and report the performance evaluation in comparison with previous prototypes. Neuromorphic processors are hardware dedicated to spiking neural networks (SNNs) to accelerate SNN operations with low-power consumption. Early proposed digital neuromorphic processors define SNN topology in a neuron-centric manner in full support of topology reconfiguration. However, this high reconfigurability comes at the cost of large memory usage, and the state-of-the-art SNN topology barely needs such high reconfigurability as for convolutional SNNs (Conv-SNNs). Further, neuron-centric routing methods hardly allow weight-reuse for Conv-SNNs. To address these concerns, we propose the layer-centric event-routing architecture (LaCERA) that uses layers (or sub-layers) as the granularity of topology unlike neuron-centric routing methods. LaCERA supports the high reconfigurability of Conv-SNN topology and high efficiency in memory usage given the use of lightweight lookup tables for event-routing and high weight-reuse rate. To evaluate LaCERA, we implemented a neuromorphic processor with 32 cores, each of which employs LaCERA, in a field-programmable gate array. The evaluation on the processor level highlights (i) almost ideal weight-reuse rate for Conv-SNNs, (ii) high efficiency in event-routing memory usage, ca. 100 × that of Loihi, and (iii) high flexibility of layer partitioning into sub-layers over multiple cores. Further, our neuromorphic processor achieved approximately a 10 × improvement in inference speed compared with graphics processing units (TITAN RTX and RTX A6000). [ABSTRACT FROM AUTHOR]

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics. [ABSTRACT FROM AUTHOR]