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A 23-µJ-per-frame All-on-Chip TinyML U-Net Processor for Real-Time Autonomous Image Segmentation in Miniaturized

Zhiye Song, Ulkuhan Guler, Anantha Chandrakasan

    IEEE Transactions on Biomedical Circuits and Systems
    |March 23, 2026
    PubMed
    Summary

    This study introduces a novel integrated processor for autonomous medical image segmentation on wearable devices. It achieves high accuracy and efficiency for real-time applications like bladder monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Computer Engineering
    • Artificial Intelligence

    Background:

    • Autonomous medical image segmentation is crucial for wearable ultrasound patches, requiring on-device processing for privacy and functionality.
    • Existing binary neural network (BNN) accelerators lack features for medical-grade segmentation, facing accuracy, utilization, and memory challenges.
    • U-Net architectures offer high performance, but their computational demands are challenging for resource-constrained wearable devices.

    Purpose of the Study:

    • To develop a fully-integrated U-Net processor for efficient, on-device medical image segmentation in wearable ultrasound patches.
    • To overcome the limitations of existing BNN accelerators in supporting accurate and computationally efficient medical image segmentation.
    • To enable real-time, autonomous monitoring capabilities in wearable medical devices through specialized hardware.

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    Main Methods:

    • Designed a 0.81 mm² integrated processor in 28nm technology featuring mixed-precision datapaths (binary convolution with 4-bit skip connections).
    • Conducted systematic design space exploration across 9,390 configurations to optimize energy-latency tradeoffs.
    • Implemented interleaved memory representation, halo reuse, hardware-supported layer fusion, and lossless compression to eliminate external memory dependency.

    Main Results:

    • The processor achieves 13.4 frames per second (fps) and 23 µJ per frame on bladder and fetal head segmentation datasets.
    • Demonstrated significant reductions in peak on-chip memory usage by 3.16× (layer fusion) and 1.38× (lossless compression).
    • Validated the processor's capability for clinical accuracy and energy-efficient, battery-powered operation in wearable medical devices.

    Conclusions:

    • The developed integrated U-Net processor enables accurate and efficient on-device medical image segmentation for wearable ultrasound applications.
    • The mixed-precision design and memory optimization techniques address key limitations of prior BNN accelerators for medical use.
    • This hardware advancement facilitates real-time, autonomous patient monitoring, enhancing privacy and accessibility of advanced medical diagnostics.