ISBN: 9786630155402 - verilog hdl for artificial intelligence hardware: designing mac engine for neural network acceleration (10 Ergebnisse)

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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware 72 pp. Englisch.

Taschenbuch. Zustand: Neu. Verilog HDL for Artificial Intelligence Hardware | Designing MAC Engine for Neural Network Acceleration | Anil Kumar Sahu (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630155402 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…