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Related Concept Videos

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A 40-nm 3.9mW, 200words/Min Neural Signal Processor in Speech Decoding for Brain-Machine Interface.

Tun-Yu Chang, Jeng-Bang Wang, Yu-Hsuan Tsai

    IEEE Transactions on Biomedical Circuits and Systems
    |November 13, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel neural signal processor for brain-machine interfaces (BMI), significantly enhancing speech decoding efficiency and speed. The processor reduces energy consumption and memory size, enabling faster, more accurate communication for users.

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    Area of Science:

    • Neuroscience
    • Computer Engineering
    • Biomedical Engineering

    Background:

    • Brain-machine interface (BMI) technology facilitates direct communication between the human brain and external devices.
    • Real-time speech decoding is a critical application for BMI, enabling communication for individuals with severe motor impairments.

    Purpose of the Study:

    • To develop an efficient neural signal processor for real-time speech decoding in brain-machine interfaces.
    • To optimize energy consumption, memory footprint, and processing latency for neural signal processing.

    Main Methods:

    • Implemented speech attempt detection to reduce energy consumption and channel requirements.
    • Utilized sparse encoding and mixed-precision arithmetic for weight encoding, reducing memory size.
    • Employed computation reordering, partial sum caching, and exploitation of input/weight sparsity in the processing element (PE) array to decrease latency.
    • Introduced an approximate top-k selection architecture in the beam search engine.

    Main Results:

    • Achieved a 46% reduction in energy consumption and decreased channels from 128 to 16 for speech attempt detection.
    • Reduced neural network off-chip memory size by 80% and processing latency by 55% through optimized encoding and computation.
    • PE array sparsity exploitation lowered latency by 95%, and mixed-precision multipliers reduced area by 27%.
    • Attained a phone error rate of 16.6% and a word error rate of 23.5% for speech decoding.
    • Demonstrated a maximum communication rate of 200 words/min, 16.7-to-42.6x faster than state-of-the-art, decoding up to 125,000 words.

    Conclusions:

    • The proposed neural signal processor significantly advances real-time BMI capabilities for speech decoding.
    • The optimizations in energy, memory, and latency enable unprecedented decoding speeds and capacity.
    • This technology holds promise for improving communication accessibility for individuals with speech and motor impairments.