Paper Key : IRJ************260
Author: Sudheer Kandula,Sree Ranga Vasudha Moda
Date Published: 21 Oct 2023
Abstract
Network optimization is indispensable for AI workloads as it accelerates data transfer, reduces model training time, and minimizes inference latency. Efficient network utilization ensures scalability, maximizes resource utilization, and enhances cost efficiency by minimizing infrastructure requirements. This is particularly crucial in the era of distributed and edge computing, where seamless communication between nodes and devices is essential for the smooth functioning of AI applications. In essence, network optimization is a linchpin for realizing the full potential of AI, influencing both the speed and cost-effectiveness of model development, training, and real-time inference.This paper dives deep into the Hardware Strategies for Network Optimization, focusing on their pivotal role. Within the hardware domain, we explore strategies like Memory, Storage, Accelerators, Network device selection. These perspectives offer a comprehensive understanding of the intricacies involved in achieving efficient communication and collaboration of massive workloads. The paper provides valuable insights into the nuances of network optimization, aiming to empower organizations to unleash the full potential of AI technologies.
DOI LINK : 10.56726/IRJMETS45389 https://www.doi.org/10.56726/IRJMETS45389