Design Space Exploration of Many-Core Processor for High-Speed Cluster Estimation

Abstract

This paper implements and improves the performance of high computational subtractive clustering algorithm using a single instruction, multiple data (SIMD) based many-core processor. In addition, this paper implements five different processing element (PE) architectures (PEs=16, 64, 256,1,024,4,096) to select an optimal PE architecture for the subtractive clustering algorithm by estimating execution time and energy efficiency. Experimental results using two different medical images and three different resolutions (128x128, 256x256, 512x512) show that PEs=4,096 achieves the highest performance and energy efficiency for all the cases.