Zhile Li

Rutgers, The State University of New Jersey

Papers

Total Citations

H-Index

About

Zhile Li is a rising researcher at the intersection of neuromorphic computing and the Internet of Things (IoT), with a focus on energy-efficient hardware design. Their most-cited work introduces a novel lightweight hybrid analog-digital spiking neural network (SNN) tailored for IoT applications. In this architecture, the artificial neural network (ANN) component operates digitally via Field-Programmable Gate Array (FPGA) technology, while the SNN component functions analogically using passive circuits—a pioneering approach that marries the precision of digital computation with the low-power advantages of analog processing. This design addresses critical challenges in deploying intelligent systems on resource-constrained edge devices. Though early in their career, Li’s work has already garnered attention, with their flagship 2024 paper accumulating citations that signal growing interest in hybrid neuromorphic systems. By bridging ANN and SNN paradigms, Li is contributing to the next generation of efficient, real-time AI for smart sensors and wearable technology. Their research holds promise for enabling autonomous, low-latency decision-making in environments where power and computational budgets are severely limited.