A subranging nonuniform sampling memristive neural network-based analog-to-digital converter
Hao You, Amirali Amirsoleimani, Jianxiong Xu, Mostafa Rahimi Azghadi, Roman Genov
Abstract
This work presents a novel 4-bit subranging nonuniform sampling (NUS) memristive neural network-based analog-to-digital converter (ADC) with improved performance trade-off among speed, power, area, and accuracy. The proposed design preserves the memristive neural network calibration and utilizes a trainable memristor weight to adapt to device mismatch and increase accuracy. Rather than conventional binary searching, we adopt quaternary searching in the ADC to realize subranging architecture’s coarse and fine bits determination. A level-crossing nonuniform sampling (NUS) is introduced to the proposed ADC to enhance the ENOB under the same resolutions, power, and area consumption. Area and power consumption are reduced through circuit sharing between different stages of bit determination. The proposed 4-bit ADC achieves a highest ENOB of 5.96 and 5.6 at cut-off frequency (128 MHz) with power consumption of 0.515 mW and a figure of merit (FoM) of 82.95 fJ/conv.