0.50-V Ultra-Low-Power Σ Δ Modulator for Sub-nA Signal Sensing in Amperometry

Szczęsny, Szymon; Kropidłowski, Marek; Naumowicz, Mariusz

doi:10.1109/jsen.2020.2974701

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Most applications use the strong inversion mode, which means that the gate voltage of the MOS transistor with respect to the source voltage (V GS ) satisfies the condition: V GS > V T + 100 mV, where V T is the technology threshold voltage. Lowering the V GS voltage to the range V T + 100 mV > V GS > V T − 100 mV will enter the moderate inversion mode [38]. By lowering the supply voltage further to the V GS < V T − 100 mV range, it enters the weak inversion mode.…”

Section: Weak Inversion Modementioning

confidence: 99%

CMOS Perceptron for Vesicle Fusion Classification

Naumowicz¹,

Pietrzak²,

Szczęsny³

et al. 2022

Electronics

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Edge computing (processing data close to its source) is one of the fastest developing areas of modern electronics and hardware information technology. This paper presents the implementation process of an analog CMOS preprocessor for use in a distributed environment for processing medical data close to the source. The task of the circuit is to analyze signals of vesicle fusion, which is the basis of life processes in multicellular organisms. The functionality of the preprocessor is based on a classifier of full and partial fusions. The preprocessor is dedicated to operate in amperometric systems, and the analyzed signals are data from carbon nanotube electrodes. The accuracy of the classifier is at the level of 93.67%. The implementation was performed in the 65 nm CMOS technology with a 0.3 V power supply. The circuit operates in the weak-inversion mode and is dedicated to be powered by thermal cells of the human energy harvesting class. The maximum power consumption of the circuit equals 416 nW, which makes it possible to use it as an implantable chip. The results can be used, among others, in the diagnosis of precancerous conditions.

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Section: Weak Inversion Modementioning

confidence: 99%

CMOS Perceptron for Vesicle Fusion Classification

Naumowicz¹,

Pietrzak²,

Szczęsny³

et al. 2022

Electronics

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“…We collected the key parameters of the learning algorithms and assessed the complexity of the algorithms. We hope that this work will be useful mainly for researchers considering the use of neurocomputation in the analysis of data from various types of sensors [ 36 , 37 , 38 ]. In chapter two, we will briefly discuss the most common neuron models, synapse models, and input encoding types.…”

Section: Introductionmentioning

confidence: 99%

Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities

Pietrzak

Szczęsny

Huderek

et al. 2023

Sensors

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Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity.

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“…The conversion of analog currents into their digital form requires using CMOS Analog-Digital Converter (ADC) matrices dedicated to the analysis of currents of sub-nA values. Circuits based on RD modulators operating in weak-inversion mode can act as such converters [19]. The role of the classifier of digital representations of fusion signals is played by a GPU (Graphics Processing Unit) system, on which a matrix of classifiers based on neural networks (NN-C) was implemented.…”

Section: Introductionmentioning

confidence: 99%

Exocytotic vesicle fusion classification for early disease diagnosis using a mobile GPU microsystem

Szczęsny

Pietrzak

2021

Neural Comput & Applic

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This work addresses monitoring vesicle fusions occurring during the exocytosis process, which is the main way of intercellular communication. Certain vesicle behaviors may also indicate certain precancerous conditions in cells. For this purpose we designed a system able to detect two main types of exocytosis: a full fusion and a kiss-and-run fusion, based on data from multiple amperometric sensors at once. It uses many instances of small perceptron neural networks in a massively parallel manner and runs on Jetson TX2 platform, which uses a GPU for parallel processing. Based on performed benchmarking, approximately 140,000 sensors can be processed in real time within the sensor sampling period equal to 10 ms and an accuracy of 99$$\%$$ % . The work includes an analysis of the system performance with varying neural network sizes, input data sizes, and sampling periods of fusion signals.

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0.50-V Ultra-Low-Power Σ Δ Modulator for Sub-nA Signal Sensing in Amperometry

Cited by 10 publications

References 22 publications

CMOS Perceptron for Vesicle Fusion Classification

CMOS Perceptron for Vesicle Fusion Classification

Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities

Exocytotic vesicle fusion classification for early disease diagnosis using a mobile GPU microsystem

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