Logarithmic Neural Network Data Converters using Memristors for Biomedical Applications

Data converters are ubiquitous in electrical data driven systems, where they are heterogeneously distributed across the analog-digital interface. Unfortunately, conventional data converters trade off speed, power, and accuracy. Logarithmic analog-to-digital/digital-to-analog converters (ADC/DACs) are employed in biomedical applications where signals with high dynamic range are recorded. For the same input dynamic range of a linear ADC/DAC, a logarithmic one can efficiently quantize the sampled data by reducing the number of resolution bits, sampling rate, and power consumption, albeit with reduced accuracy for high amplitudes. Previously, we employed novel neural network architectures to design smart data converters that could be trained in real-time for general purpose applications, breaking through the speed-power-accuracy tradeoff, and using machine learning techniques and memristors for synaptic realization. In this paper, we report the results of SPICE simulations performed to train our converters to perform logarithmic quantization. The proposed architecture achieved a 77.19 pJ/conv FOM, 2.55 ENOB, 0.26 LSB INL, and 0.62 LSB DNL. These promising features will pave the way towards adaptive human-machine interfaces with continuous varying conditions for precision medicine applications.