A Low-Power Level-Crossing Analog-to-Spike Converter Intended for Neuromorphic Biomedical Applications

The increasing interests in building bio-signal recording and processing systems for personal healthcare applications have been hindered by the critical sampling energy consumption issues of conventional biomedical systems. To address these limits, we propose a comprehensive strategy centered around a low-power level-crossing analog-to-spike converter (LC-ASC). This strategy enables event-driven compressive sampling by leveraging signal sparsity, achieving lower average sampling rates than Nyquist sampling. Our strategy includes universal VerilogA LC-ASC models, evaluation tools, and a reconfigurable data interface for versatile digital processing. Specifically, we introduce an online open-source VerilogA LC-ASC model and compression performance calculation tools for evaluating its performance with different bio-signals. The implemented LC-ASC chip demonstrates very-low power consumption of 31.5125.3 nW validated through chip measurements. Additionally, the proposed reconfigurable data interface ensures seamless integration with synchronous and asynchronous digital processing modules without sacrificing system-level performance. These advancements pave the way for energy-efficient neuromorphic biomedical circuits and systems.