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A hardware-efficient on-implant spike compression processor based on VQ-DAE for brain-implantable microsystems.

Nazanin Ahmadi-Dastgerdi1, Hossein Hosseini-Nejad2, Hamid Alinejad-Rokny3

  • 1Faculty of Electrical Engineering, K. N. Toosi University of Technology, P.O. Box 16315-1355, Tehran, 1631714191, Iran.

Medical & Biological Engineering & Computing
|February 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel denoising autoencoder (DAE) enhanced vector quantization (VQ) processor for neural recording microsystems. The VQ-DAE approach improves spike compression accuracy and hardware efficiency for implantable devices.

Keywords:
Brain-implantable microsystemsDenoising autoencodersSpike compressionVector quantization

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Integrated Circuits

Background:

  • High-density neural recording microsystems generate vast amounts of data, necessitating efficient data compression for wireless transmission.
  • Current spike compression methods face challenges in balancing reconstruction accuracy with hardware efficiency for implantable systems.

Purpose of the Study:

  • To enhance the performance of vector quantization (VQ) based spike compression using denoising autoencoders (DAE).
  • To develop a hardware-efficient multi-channel architecture for the proposed VQ-DAE processor suitable for neural implants.

Main Methods:

  • Integration of denoising autoencoders (DAE) with vector quantization (VQ) for spike compression.
  • Design and implementation of a hardware-efficient multi-channel processor architecture.
  • Fabrication using 180-nm CMOS technology.

Main Results:

  • Achieved an average signal-to-noise-distortion (SNDR) of 14.51 dB at a spike compression ratio (SCR) of 30.
  • The processor operates at 192 kHz and 1.8 V, consuming 4.88 power and occupying 0.14 mm2 silicon area per channel.
  • Validation confirmed satisfactory performance in terms of reconstruction accuracy and hardware efficiency.

Conclusions:

  • The proposed VQ-DAE approach offers a significant improvement in spike compression for neural recording microsystems.
  • The developed hardware-efficient architecture is suitable for implantable devices, minimizing power consumption and silicon area.
  • This technology advances the feasibility of high-density neural implants by addressing data transmission bottlenecks.