Optimization of Memory requirement using Reinforcement Learning based on Cognitive Science in Neuromorphic VLSI Chips

Abstract

Software industry is ever evolving the pressure on hardware industry for newlineincorporating more ICs in a single chip is increasing Scaling in VLSI chips is finding its limitations due to secondary effects Moreover the demand to be intelligent is posing new challenges for chip manufacturers The only way to mimic human intelligence in machines is to build the machines based on architectures of nervous system The sudden realization of need to modify the machines at architectural level to exhibit intelligence has led many investigations in biological systems newline newline