On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and (1,s)-sparse random binary matrix [(1,s) -SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and (1,s)-SRBM encoders with reduced area and total power consumption.

I. Introduction

Real-world behaviors involve simultaneous activation of large population of selective neurons in patterns that cannot necessarily be characterized by studying individual neurons one at a time. Micro-electrode fabrication and high-density recorders have dramatically increased the number of recording channels in various recording modalities [1], while further efforts are required to address processing and transmitting large-scale brain signals in real-time to find causal relationship within the high-dimensional dataset defined by the multi-channel activities and complex behaviors. For instances, the capabilities of wireless recording of neuronal dynamics [2]–[5] from different modalities are highly desired for the understanding of individual neurons, local circuits and network functions in freely behaving animal experiments, or studying the cortical phenomena and performing clinical diagnosis, and developing advanced brain machine interfaces without tethered wire connections.