Design of Variation Tolerant Near Threshold Processor Using Artificial Ecosystem Optimizer with Hybrid Deep Learning

Abstract

Recently, several applications of data mining and pattern recognition require statistical signal processing (SP) to be used and machine learning (ML) techniques for processing massive data volumes in energy-constrained contexts. It is developing interest in executing difficult ML techniques like convolutional neural network (CNN) on lesser power embedding environments to allow on-device learning and inference. Several of these platforms are that utilized as lower power sensor nodes with lower to medium throughput conditions. Near threshold processor (NT) proposals are appropriate for these applications where as affected by a vital enhancement in variants. This research offers an Artificial Ecosystem Optimizer with Hybrid Deep Learning for Variation-Tolerant Near-Threshold Processor (AEOHDL-VTNT). The inference of embedded systems at the network edge serves as the foundation for the AEOHDL-VTNT approach that is being discussed. In the described AEOHDL-VTNT approach involves two primary processes: HDL-based VTNT design and hyper parameter tweaking. Initially, the HDL model is used to develop the VTNT. Next, in the second step, the AEO method is used for hyper parameter tweaking of the HDL model, which improves the HDL's overall performance. A number of simulations were carried out to show how the AEOHDL-VTNT approach improved performance. The simulation results showed that the AEOHDL-VTNT approach outperformed other models.