Approximate Computing Based Low-Power FPGA Design for Big Data Analytics in Cloud Environments

Abstract

As cloud computing continues to evolve, the demand for scalable and energy-efficient infrastructure to handle extensive applications becomes paramount. Traditional transistor scaling and microprocessor design methods no longer suffice to meet the growing scale of cloud usage. This research explores the potential of approximate computing (AC) as an innovative solution to these challenges, particularly in high-demand computational settings. AC, known for its ability to make controlled accuracy trade-offs, is identified as a key strategy for improving both the performance and energy efficiency of cloud infrastructure, with a focus on low-power Field-Programmable Gate Array (FPGA) designs. This paper introduces novel methodologies that harness the strengths of AC, emphasizing its application in neural-based and machine-learning techniques for energy-efficient solutions. By targeting the performance of AC, especially in varied application domains and complex data mining scenarios, we propose two groundbreaking approaches that significantly enhance computational speed and reduce energy consumption. Our empirical analysis demonstrates notable improvements over existing techniques, highlighting the effectiveness of AC in optimizing cloud infrastructure. The proposed model on FPGA through cloud computing attains substantial elevation rates by 89 % and energy reduction by 122%, which had been good outcomes. This study not only confirms the benefits of integrating AC with low-power FPGA designs for cloud environments but also sets a new benchmark for future research in achieving more sustainable and efficient cloud computing via VLSI FGPA design analysis solutions.