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

Abstract

High-density implantable neural recording microsystems deal with a huge amount of data. Since the wireless transmission of the raw recorded data leads to excessive bandwidth requirements, spike compression approaches have become vital to such systems. The compression processor is designed to be implemented on the implant and so to avoid any tissue damage, the hardware cost of the processor is of great importance. The vector quantization (VQ) algorithm has proven to be effective in compression applications and spike compression systems as well. In this paper, benefiting from the capabilities of the denoising autoencoders (DAE), we propose a solution to enhance the compression performance of the VQ-based approach in terms of both reconstruction accuracy and hardware efficiency. Moreover, we develop a hardware-efficient multi-channel architecture for the proposed VQ-DAE processor. The processor has been implemented in a 180-nm CMOS technology and the validation and verification processes confirm that it provides satisfactory results. It achieves an average signal-to-noise-distortion (SNDR) of 14.51 at a spike compression ratio (SCR) of 30. Operated at a clock frequency of 192 kHz and a supply voltage of 1.8 V, the circuit consumes a power of 4.88 $\mu W$ and a silicon area of 0.14 mm² per channel.