An Energy-efficient Wireless Neural Recording System with Compressed Sensing and Encryption
Xilin Liu, Andrew G. Richardson, and Jan Van der Spiegel

TL;DR
This paper introduces an energy-efficient wireless neural recording system that combines compressed sensing for data compression and encryption, along with ECC for secure key exchange, achieving high power savings and robust security.
Contribution
It presents a novel integrated system with custom CMOS design and low-power cryptography for secure, energy-efficient neural data transmission.
Findings
Achieves 8x data compression with 0.973 correlation coefficient
Provides 35x power savings over conventional systems
Ensures security with ciphertext-only attack resistance
Abstract
This paper presents a wireless neural recording system featuring energy-efficient data compression and encryption. An ultra-high efficiency is achieved by leveraging compressed sensing (CS) for simultaneous data compression and encryption. CS enables sub-Nyquist sampling of neural signals by taking advantage of its intrinsic sparsity. It simultaneously encrypts the data with the sampling matrix being the cryptographic key. To share the key over an insecure wireless channel, we implement an elliptic-curve cryptography (ECC) based key exchanging protocol. The CS operation is executed in a custom-designed IC fabricated in 180nm CMOS technology. Mixed-signal circuits are designed to optimize the power efficiency of the matrix-vector multiplication (MVM) of the CS operation. The ECC algorithm is implemented in a low-power Cortex-M0 microcontroller (MCU). To be protected from timing and power…
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Taxonomy
TopicsQuantum-Dot Cellular Automata · Advanced Memory and Neural Computing · Low-power high-performance VLSI design
