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About

Lingli Cheng is pioneering the next generation of neuromorphic computing by engineering bioinspired hardware that mimics the brain’s sensory and neural circuits. Her research focuses on developing heterogeneously integrated spiking neuron arrays and memristor-based systems for multimodal perception, object classification, and robotic intelligence. Cheng’s most influential work, a 2022 study on a heterogeneously integrated spiking neuron array for multimode-fused perception and object classification, has garnered 165 citations, establishing a foundation for compact, low-power sensory processing systems that emulate the human somatosensory system. She further advanced the field with a 2024 paper on firing feature-driven neural circuits using scalable memristive neurons for robotic obstacle avoidance (45 citations), demonstrating how neuronal firing dynamics can be harnessed for real-time autonomous navigation. Her 2022 bioinspired configurable cochlea based on memristors (17 citations) extends this approach to auditory processing, offering a path toward highly efficient voice recognition systems. By replacing conventional CMOS technology with memristor-based architectures, Cheng is enabling scalable, energy-efficient hardware that bridges biological neural principles and artificial intelligence, with direct applications in robotics, sensory prosthetics, and edge computing.