Efficient Power Optimization in CNN Acceleration through Innovative Hardware Design

Abstract

In the expanding realm of image recognition tasks, Convolutional Neural Networks (CNNs) have risen as indispensable tools, penetrating domains from autonomous systems to medical diagnostics. However, as their range of applications expands, the demand for effective hardware implementations grows. The Tiny YOLO architecture is the primary subject of this paper's analysis since it introduces a revolutionary power-efficient design paradigm specifically suited for CNNs. A Wallace Tree Multiplier and a Brent Kung Adder, which are deliberately positioned to reduce resource utilization and power consumption while maintaining computing prowess, are meticulously woven into our method. Empirical analyses support the effectiveness of our strategy, highlighting significant energy savings together with computational effectiveness, finally resulting in a comprehensive and workable method for power-efficient CNN acceleration. Our method has the potential to revolutionize the field of energy-efficient deep learning by combining the inherent capabilities of the Tiny YOLO architecture with smartly designed hardware components.