A Novel Workload-Aware and Optimized Write Cycles in NVRAM

With the emergence of the Internet of things(IoT),embedded systems have now changed its dimensionality and it is applied in various domains such as healthcare,home automation and mainly Industry 4.0.These Embedded IoT devices are mostly battery-driven.It has been analyzed that usage of Dynamic Random-Access Memory(DRAM)centered core memory is considered the most significant source of high energy utility in Embedded IoT devices.For achieving the low power consumption in these devices,Non-volatile memory(NVM)devices such as Parameter Random Access Memory(PRAM)and Spin-Transfer Torque Magnetic RandomAccess Memory(STT-RAM)are becoming popular among main memory alternatives in embedded IoT devices because of their features such as high thickness,byte addressability,high scalability and low power intake.Additionally,Non-volatile Random-Access Memory(NVRAM)is widely adopted to save the data in the embedded IoT devices.NVM,flash memories have a limited lifetime,so it is mandatory to adopt intelligent optimization in managing the NVRAM-based embedded devices using an intelligent controller while considering the endurance issue.To address this challenge,the paper proposes a powerful,lightweight machine learning-based workload-adaptive write schemes of the NVRAM,which can increase the lifetime and reduce the energy consumption of the processors.The proposed system consists of three phases like Workload Characterization,Intelligent Compression and Memory Allocators.These phases are used for distributing the write-cycles to NVRAM,following the energy-time consumption and number of data bytes.The extensive experimentations are carried out using the IoMT(Internet of Medical things)benchmark in which the different endurance factors such as application delay,energy and write-time factors were evaluated and compared with the different existing algorithms.

Fuzzy Based Interleaved Step-up Converter for Electric Vehicle

This work focuses on the fuzzy controller for the proposed three-phase interleaved Step-up converter(ISC).The fuzzy controller for the proposed ISC converters for electric vehicles has been discussed in detail.The proposed ISC direct current(DC-DC)converter could also be used in automobiles,satellites,industries,and propulsion.To enhance voltage gain,the proposed ISC Converter combines boost converter and interleaved converter(IC).This design also reduces the number of switches.As a result,ISC converter switching losses are reduced.The proposed ISC Converter topology can produce a 143 V output voltage and 1 kW of power.Due to the high voltage gain of this converter design,it is suitable for medium and high-power systems.The proposed ISC Converter topology is simulated in MATLAB/Simulink.The simulated output displays a high output voltage.But the output voltage contains maximum ripples.Fuzzy proposes an ISC Converter which makes closed loop responsiveness and reduces the output voltage ripple.The proposed ISC converter has the lowest ripple output voltage,which is less than 2%,because the duty cycle is regulated using the fuzzy logic controller.It offers high voltage gain,minimal ripple,and low switching loss.The performance of the proposed converter is compared to that of the fuzzy and Pro-portional Integral(PI)controllers implemented in MATLAB.