The "Booth Encoding-Based Energy Efficient Multipliers for Deep Learning Systems" project addresses the pressing need for energy-efficient hardware solutions in deep learning. As AI applications become increasingly power-hungry, our project offers an innovative approach to tackle this challenge. By leveraging Booth encoding and Exponent-of-Two (EO2) quantization, we aim to significantly reduce energy consumption in neural network computations without compromising accuracy. This project promises to extend the battery life of portable devices and minimize the power footprint of neural network accelerators, meeting the growing demand for energy-efficient AI hardware solutions. Additionally, it is designed for effective implementation using Xilinx ISE 14.7, making it a practical and accessible solution for FPGA-based deep learning systems.