Key Takeaways & Executive Findings
- •• A novel near-sensor edge computing system integrates AlN microrings for photonic feature extraction and Si Mach–Zehnder interferometers for photonic neural network operations, achieving real-time AI processing. • Demonstrates high classification accuracy (96.77% for gestures, 98.31% for gaits) with low latency (<10 ns) and minimal energy consumption (<0.34 pJ). • Enables low-power, high-speed AI applications with seamless hybrid photonic-electronic integration on a bilayer AlN/Si waveguide platform. • Bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces.
Abstract
The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (<10 ns), and minimal energy consumption (<0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces, marking a pivotal advancement in edge computing and AI deployment.
1. Introduction
With the rapid development of Artificial Intelligence of Things (AIoT), the number of sensor nodes and the volume of sensing data have both increased dramatically [1]. This poses significant challenges to the computational capacities and energy consumption of artificial intelligence (AI) [2]. Recent emerging large-scale AI models have necessitated the establishment of dedicated AI data centers. These cloud-based computing frameworks are under immense pressure to manage the rising demands for data bandwidth, high transmission rates, extensive storage capacities, and efficient coding and decoding processes [3]. To address these limitations, edge computing has emerged as a complementary solution to cloud computing [4–7]. This framework reduces the volume of data transmitted to the cloud, thereby alleviating bandwidth and energy requirements. Additionally, edge computing offers enhanced privacy by enabling data processing closer to the source, using metadata, and minimizing the exposure of sensitive information. As AI systems evolve to meet the needs of modern applications, integrating edge computing with cloud infrastructures is becoming an essential strategy for achieving sustainable and efficient AI operations.
The traditional von Neumann architecture, which relies on logical operations performed by fundamental components like transistors, has served as the backbone of computing for decades. However, with the rapid expansion of AI applications, there has been a notable shift toward neuromorphic chips, such as memristors and memtransistors [8–12]. These devices enable parallel matrix operations and are better suited for the complex computational demands of AI workloads. Beyond foundational hardware upgrades, industry trends have increasingly focused on integrated solutions tailored for edge computing. Companies like STMicroelectronics have developed hybrid chips that combine microelectromechanical systems (MEMS) inertial sensors or microphones with memory, data buffers, and transmission capabilities, all packaged into a single unit. Besides, MEMS-based edge computing has demonstrated significant potential for edge AI applications by integrating sensing and computation within a single device [13–15]. Recent advancements include MEMS neural networks for direct sensor-to-computation processing and MEMS reservoir computing systems.
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Zhihao Ren, Zixuan Zhang, Yangyang Zhuge, Zian Xiao, Siyu Xu, Jingkai Zhou, Chengkuo Lee (2025). Near-Sensor Edge Computing System Enabled by a CMOS Compatible Photonic Integrated Circuit Platform Using Bilayer AlN/Si Waveguides. Nano-Micro Letters. https://doi.org/10.1007/s40820-025-01743-y
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Frequently Asked Questions
What is the main contribution of this paper?
The paper presents a near-sensor edge computing system using a bilayer AlN/Si waveguide platform, integrating photonic feature extraction and neural network operations for real-time, energy-efficient AI at the edge.
What are the key performance metrics achieved?
The system achieves classification accuracies of 96.77% for gestures and 98.31% for gaits, with ultra-low latency (<10 ns) and minimal energy consumption (<0.34 pJ).
How does the system work?
It leverages AlN microring resonators for photonic feature extraction and Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, enabling hybrid photonic-electronic integration.
What are the potential applications?
The system is suitable for healthcare monitoring, robotics, and next-generation human-machine interfaces, offering privacy-preserving and efficient AI solutions.
Is the platform CMOS compatible?
Yes, the platform is CMOS compatible, which facilitates integration with existing electronic systems and scalable manufacturing.
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