Novel Approaches in AI Processing Systems for their Better Reliability and Function

Abstract

It looks into new innovative architectures based on the structure and functionality of the human brain to improve AI computing systems. As AI uses become more sophisticated and required, common computer hardware architectures have problems with power consumption, scalability and efficiency. These challenges can be effectively addressed by the neuromorphic computing which incorporates the biological structure and functionality of neural networks. In this paper, several brain-related structures like spiking neural networks and synaptic plasticity are discussed to design new effective and resource-sharing systems. These architectures attempt to emulate particular aspects of the brain, such as sparse coding, event-driven processing and real-time learning all in an effort to reduce power consumption while increasing processing speed as well as flexibility. The study also explores the usage
of additional hardware components, including memristors and neuromorphic processors, to enhance core AI applications, including pattern identification, decision-making, and sensory analysis. This has been shown through simulations and prototyping hardware using six hardware implements exhibiting great improvements of computational effectiveness and execution compared
to regular architectures. This work is a useful addition toward the continuous advancement of next generation AI systems, which presents a way forward toward AI hardware that is efficient, scalable, and closely modeled after biological neural structures that would effectively support complex machine learning in real life situations.