Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications

• URL: http://arxiv.org/abs/2504.06996v1
• Date: Wed, 09 Apr 2025 16:09:00 GMT
• Title: Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications
• Authors: Adithya Krishna, Sohan Debnath, André van Schaik, Mahesh Mehendale, Chetan Singh Thakur,
• Abstract summary: Large-scale brain recordings produce vast amounts of data that must be wirelessly transmitted for offline analysis and decoding.<n>We propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs)<n>The compressed neural data from RAMAN is reconstructed offline with superior signal-to-noise and distortion ratios (SNDR) of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings.
• Score: 2.5655109786707717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces (BCIs) utilizing high-density intracortical recordings with hundreds or thousands of electrodes. However, transmitting raw neural data presents significant challenges due to limited communication bandwidth and resultant excessive heating. To address this challenge, we propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs). The CAE encoder section is implemented on RAMAN, an energy-efficient tinyML accelerator designed for edge computing, and subsequently deployed on an Efinix Ti60 FPGA with 37.3k LUTs and 8.6k register utilization. RAMAN leverages sparsity in activation and weights through zero skipping, gating, and weight compression techniques. Additionally, we employ hardware-software co-optimization by pruning CAE encoder model parameters using a hardware-aware balanced stochastic pruning strategy, resolving workload imbalance issues and eliminating indexing overhead to reduce parameter storage requirements by up to 32.4%. Using the proposed compact depthwise separable convolutional autoencoder (DS-CAE) model, the compressed neural data from RAMAN is reconstructed offline with superior signal-to-noise and distortion ratios (SNDR) of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings.

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