A high performance scalable fuzzy based modified Asymmetric Heterogene Multiprocessor System on Chip (AHt-MPSOC) reconfigurable architecture

Abstract

This Ensuing generation of FPGA circuit tolerates the combination of lot of hard and soft cores as well as devoted accelerators on a chip. The Heterogene Multi-Processor System-on-Chip (Ht-MPSoC) architecture accomplishes the requirement of modern applications. A compound System on Chip (SoC) system designed for single FPGA chip, and that considered for the performance/power consumption ratio. In the existing method, a FPGA based Mixed Integer Programming (MIP) model used to define the Ht-MPSoC configuration by taking into consideration the sharing hardware accelerator between the cores. However, here, the sharing method differs from one processor to another based on FPGA architecture. Hence, high number of hardware resources on a single FPGA chip with low latency and power targeted. For this reason, a fuzzy based MIP and Graph theory based Traffic Estimator (GTE) are proposed system used to define New asymmetric multiprocessor heterogene framework on microprocessor (AHt-MPSoC) architecture. The bandwidths, energy consumption, wait and transmission range are better accomplished in this suggested technique than the standard technique and it is also implemented with a multi-task framework. The new Fuzzy control-based AHt-MPSoC analysis proves significant improvement of 14.7 percent in available bandwidth and 89.8 percent of energy minimized to various traffic scenarios as compared to conventional method.

Design Space Exploration of HW Accelerators and Network Infrastructure for FPGA-Based MPSoC

“…For example, for 8-processors, only the configurations with 2, 4 or 8 shared hardware accelerators are considered. In [19], the authors propose a framework to explore the design of multiprocessor heterogeneous architecture based on fuzzy MIP and Graph theory based Traffic Estimator (GTE). The authors propose to share HWAcc, through the integration of bridges and a fuzzy-controller capable of solving sharing problems.…”

Internet of Things‐based root disease classification in alfalfa plants using hybrid optimization‐enabled deep convolutional neural network

“…In general, machine learning and the deep neural networks expand its ability for the accurate prediction of plant phenotype 9 . The technological advancement 10 makes the researchers to collect different genetic diversity that the task that has been complex in past by providing the amount of time as well as effort employed in the manual analysis. Various recent works used the deep learning methods 11 for quantifying and finding the ground plant traits, like count of leaves in the rosette plants by considering the images having more resolution 12 .…”

Active control of ripple energy in single-phase pulse width modulated rectifier

“…An auxiliary capacitor voltage significantly greater than the dc bus voltage may be used to monitor the reference current in this boost buck converter. To monitor the reference current in this boost buck converter, an auxiliary capacitor voltage may be much higher than the dc bus voltage (Raveendran et al 2021;Amiri et al 2020). Corte-Rodrı ´guez et al 2012 for DC voltage regulation and power factor improvement developed a nonlinear controller.…”