Computational Failure Analysis of In-Memory RRAM Architecture for Pattern Classification CNN Circuits

Prabhu, Nagaraj Lakshmana; Raghavan, Nagarajan

doi:10.1109/access.2021.3136193

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Cited by 4 publications

(2 citation statements)

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“…Two endpoints of ( x 1 , y 1 ) and ( x 2 , y 2 ) are selected, and the current at a specific time of x is y t . Therefore, the ON and OFF currents of the memory after 10 years can be estimated using the following equation: 43 …”

Section: Resultsmentioning

confidence: 99%

Effect of stoichiometry on the resistive switching characteristics of STO resistive memory

et al. 2023

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Section: Resultsmentioning

confidence: 99%

Effect of stoichiometry on the resistive switching characteristics of STO resistive memory

et al. 2023

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“…The WDT-RPMS shall use RRAM-based computing (currently prototyping on Raspberry pi 4B and/or NVIDIA Jetson Nano microcontrollers). The cost of the cooling system shall be reduced due to RRAM revitalizing the data centers by utilizing the RRAM-based Convolutional Neural Networks (CNN) system [21].…”

Section: Computingmentioning

confidence: 99%

A pathway to In-Memory Computing driven Wellness Digital Twin utilizing Remote Patient Monitoring System (WDT-RPMS)

Kadari¹,

Sichani²,

Moreno³

2023

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With fast-paced digital transformation, precision medicine has been an essential part to elevate the conscience of healthcare with respect to the biopsychosocial model with the help of Remote Patient Monitoring Systems. Digital twins have been successful in many industries, in healthcare, it can optimize the wellness of the human body to improve physical and mental wellness including the non-diagnosed diseased populations and rural populations. In this paper, we discuss the challenges in terms of computing to implement a complex system called Wellness Digital Twin-based Remote Patient Monitoring System following Model-Based Systems Engineering (MBSE), we present the definition, needs of the digital twin in the context of wellness and healthcare, a feasibility study followed by a concept of operations. Subsequently, we present the requirements and architecture including In-memory computing, (Emerging NonVolatile Memories, memristive devices) that will enhance sensing, analytics, and levers of action.

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Ferroelectric materials for neuroinspired computing applications

Wang

Hao

Dkhil

et al. 2024

Fundamental Research

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Computational Failure Analysis of In-Memory RRAM Architecture for Pattern Classification CNN Circuits

Cited by 4 publications

References 38 publications

Effect of stoichiometry on the resistive switching characteristics of STO resistive memory

Effect of stoichiometry on the resistive switching characteristics of STO resistive memory

A pathway to In-Memory Computing driven Wellness Digital Twin utilizing Remote Patient Monitoring System (WDT-RPMS)

Ferroelectric materials for neuroinspired computing applications

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