EXPLORING THE POTENTIAL OF NEUROMORPHIC COMPUTING TO ENHANCE EFFICIENCY AND PERFORMANCE IN AI WORKLOADS

Abstract

The increasing processing needs of artificial intelligence (AI) has inspired utilizing the architecture of the human brain as a viable way to address this challenge and helped neuromorphic computing become a reality. In contrast to von Neumann architecture-based computer systems, neuromorphic systems offer event driven parallel processing which can make functionally compute thousands more times in parallel and with much lower energy consumption compared to today's technologies. The scope of this work is to study how neuromorphic computing may enable the performance and effectiveness of AI tasks that rely on demanding resources, such as in image recognition, natural language processing and real-time data processing. On the basis of a proper examination of arrangements of the present in neuromorphic equipment, for example, IBM's True North, Brain Chip’s Akida and Intel's Loihi, it tends to be contended that the framework is thoroughly greater than when contrasted and customary GPUs and CPUs as far as power utilization and fundamentally increasingly proficient. The integration of spiking neural networks (SNNs) into neuromorphic systems has been demonstrated to improve real time job performance and offer important advantages for future edge AI applications where energy efficiency and delay are essential. In addition, to facilitate data interpretation and decision making in an edge database system, the work studies the possibility for synergy between neuromorphic computing and a hybrid AI model that unifies deep learning with symbolic reasoning. For instance, the research showed that neuromorphic computing and hybrid AI models have a range of practical applications in smart cities, medical diagnostics, self driving vehicles and smart agriculture among others. Although neuromorphic computing offers various advantages, there are some things to be worked out, like scaling, interfacing with existing AI pipelines, and developing a standardized programming framework. In the near future, efforts for future research should focus on establishing standardized framework s and evaluation measures to close the gap between neuromorphic systems and conventional AI architectures. Finally, neuromorphic computing and hybrid AI model allow us to take on these computational issues in modern AI workloads in a revolutionary way. With continued advancement of these technologies, the possibility of smarter, more responsive, energy efficient systems are possible: systems that can scale to support the needs of real-time applications in a number of fields.