Verilog HDL for Artificial Intelligence Hardware

The rapid growth of Artificial Intelligence (AI), Artificial Neural Networks (ANNs), and Deep Neural Networks (DNNs) has created a strong demand for high-performance, low-power, and efficient hardware accelerators. Modern neural network applications involve a large number of computational operations, where the Multiply-Accumulate (MAC) unit plays a critical role in performing multiplication and accumulation processes. Since MAC operations dominate neural computations, optimizing MAC architecture directly improves the overall performance of AI accelerators. This book presents the design and FPGA implementation of a high-speed and area-efficient pipelined MAC architecture for neural network acceleration using Verilog HDL. The proposed architecture integrates a Vedic multiplier for fast parallel partial product generation and a Binary-to-Excess-1 Converter (BEC) based Square Root Carry Select Adder (SQRT-CSLA) for efficient accumulation. This work is intended for students, researchers, FPGA designers, and VLSI professionals interested in developing efficient hardware solutions for next-generation AI and deep learning applications.