A high CMRR, high resolution bio-ASIC for ECG signals

Garje, Kiran Kumar; Kumar, Shravan; Tripathi, Amitesh Kumar; Maruthi, Gillela; Kumar, M. Madhava

doi:10.1109/isvdat.2016.8064890

Cited by 3 publications

(1 citation statement)

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“…The training algorithm ensures power consumption optimization, mismatch self-calibration, and noise tolerance [9]. However, four-bit resolution is insufficient for practical applications [10]- [12], while direct scaling of this architecture is challenging due to the quadratic increase in number of synaptic weights (with exponentially large values), large area, high power consumption, longer training time, and limited sampling frequency.…”

Section: Introductionmentioning

confidence: 99%

A Pipelined Memristive Neural Network Analog-to-Digital Converter

Danial

Sharma

Kvatinsky

2020

2020 IEEE International Symposium on Circuits and Systems (ISCAS)

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With the advent of high-speed, high-precision, and low-power mixed-signal systems, there is an ever-growing demand for accurate, fast, and energy-efficient analog-to-digital (ADCs) and digital-to-analog converters (DACs). Unfortunately, with the downscaling of CMOS technology, modern ADCs trade off speed, power and accuracy. Recently, memristive neuromorphic architectures of four-bit ADC/DAC have been proposed. Such converters can be trained in real-time using machine learning algorithms, to break through the speedpower-accuracy trade-off while optimizing the conversion performance for different applications. However, scaling such architectures above four bits is challenging. This paper proposes a scalable and modular neural network ADC architecture based on a pipeline of four-bit converters, preserving their inherent advantages in application reconfiguration, mismatch selfcalibration, noise tolerance, and power optimization, while approaching higher resolution and throughput in penalty of latency. SPICE evaluation shows that an 8-bit pipelined ADC achieves 0.18 LSB INL, 0.20 LSB DNL, 7.6 ENOB, and 0.97 fJ/conv FOM. This work presents a significant step towards the realization of large-scale neuromorphic data converters.

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Section: Introductionmentioning

confidence: 99%