A Scalable Automated Framework For Multiply-Accumulate Unit Design in High-Performance Computing Applications

Abstract

High-Performance Computing (HPC) applications require efficient and high-speed arithmetic units to handle complex computational tasks with reduced latency and power consumption. The Multiply-Accumulate (MAC) unit plays a crucial role in digital signal processing, artificial intelligence, image processing, and scientific computing systems. This paper presents a scalable automated framework for the design and implementation of MAC units optimized for high-performance computing applications. The proposed framework focuses on improving computational speed, hardware utilization, scalability, and energy efficiency through automated design techniques and modular architecture. Advanced optimization strategies such as parallel processing, pipelining, and configurable arithmetic modules are incorporated to enhance overall system performance. The framework supports flexible scalability, enabling efficient adaptation to varying computational requirements and hardware platforms. Experimental analysis demonstrates that the proposed approach achieves improved throughput, reduced delay, and optimized resource consumption compared to conventional MAC unit designs. The developed framework offers a reliable and efficient solution for nextgeneration HPC systems requiring high-speed arithmetic operations and scalable hardware architectures.