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Abstract
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.
Highlights
According to estimates by Gartner Research, in the three years, approximately 75% of data will be processed outside the cloud, i.e., with the use of edge devices [1]
We focused on the implementation of an exocytosis signal processing system using an analog CMOS (Complementary Metal-Oxide-Semiconductor) preprocessor
In the case of analysis of vesicle fusion signals such the narrow range of signals observed by means of dedicated carbon nanotube (CNT) sensors necessitates the use of reduced supply voltage modes in the computing circuits
Summary
According to estimates by Gartner Research, in the three years, approximately 75% of data will be processed outside the cloud, i.e., with the use of edge devices [1]. In the case of analysis of vesicle fusion signals such the narrow range of signals observed by means of dedicated carbon nanotube (CNT) sensors necessitates the use of reduced supply voltage modes in the computing circuits This is a big challenge, especially since the monitoring of precancerous conditions is a long process and should take place without the need to hospitalize the patient. Strong reduction of power consumption while maintaining high computing efficiency was the main goal of the presented approach For all these reasons, the paper presents the methodology of designing an edge processing system that implements the functionality of a neural network for the classification of exocytosis signals.
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