Paper Key : IRJ************843
Author: Aditya Manoj Kumar,Sagar Jayprakash Gupta
Date Published: 18 Nov 2024
Abstract
In the era of artificial intelligence and machine learning, the demand for efficient and powerful hardware accelerators is critical for real-time processing and low-power consumption in embedded systems and edge devices. Neural Processing Units (NPUs), designed to handle the high computational demands of deep learning tasks, are benchmarked by their ability to perform a vast number of operations per second. A primary metric for assessing the performance of NPUs is Tera Operations Per Second (TOPS), a measure of computational throughput representing trillions of operations per second. This paper explores the role of TOPS as a key performance metric, examining how it influences the design, optimization, and application of NPUs across various domains, from autonomous vehicles to mobile devices. Furthermore, we discuss the limitations of relying solely on TOPS, including the potential discrepancies in performance due to variations in power efficiency, memory bandwidth, and model-specific requirements. By analyzing case studies and comparing TOPS with alternative metrics, this research aims to provide a comprehensive understanding of how TOPS impacts NPU development and the broader implications for advancing AI-driven technologies.
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