Microelectronics Colloquium

Neuromorphic On-Device Intelligence for Energy-Efficient AIoT

Chang Gao

In the swiftly advancing realm of Artificial Intelligence of Things (AIoT), the integration of edge smart devices and communication networks is becoming increasingly central to our digital infrastructure. In this context, the need for energy-efficient computing—characterized by low latency and low power consumption—is paramount, not only to enhance user experience across various AIoT applications but also to contribute to carbon neutrality. Neuromorphic computing emerges as a promising, sustainable solution, offering both efficiency and effectiveness. This presentation will delve into our recent research in neuromorphic computing, focusing on its application in speech recognition, eye tracking, and robotic control. Our work underscores the potential of neuromorphic technology to substantially reduce latency and energy consumption while managing complex tasks with negligible accuracy loss. In addition, we will delve into the application of AI for the correction of non-linearity in wideband RF power amplifiers, a critical aspect of advanced RF signal processing for emerging 6G and WiFi 7 technologies vital for connecting data-intensive AIoT devices in the future. By integrating neuromorphic computing, we aim to make AIoT devices more accessible, thereby enhancing the quality of life and fostering a sustainable, environmentally friendly future.

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