Abstract

Abstract

Abstract
Segmentation is a vital step for pattern recognition systems used in in-body imaging scenarios. In this paper we compare the performance of three popular segmentation algorithms (mean shift, normalized cuts, level-sets) when applied to two distinct in-body imaging scenarios: chromoendoscopy and narrow-band imaging. Observation shows that the model-based algorithm did not perform well, when compared to its segmentation by clustering alternatives. Normalized cuts obtained the best performance although future work hints that texture similarity should be further explored in order to increase segmentation performance in this type of scenarios.

Abstract

Abstract
This paper presents a novel approach to accelerate program execution by mapping repetitive traces of executed instructions, called Megablocks, to a runtime reconfigurable array of functional units. An offline tool suite extracts Megablocks from microprocessor instruction traces and generates a Reconfigurable Processing Unit (RPU) tailored for the execution of those Megablocks. The system is able to transparently movebcomputations from the microprocessor to the RPU at runtime. A prototype implementation of the system using a cacheless MicroBlaze microprocessor running code located in external memory reaches speedups from 2.2x to 18.2x for a set of 14 benchmark kernels. For a system setup which maximizes microprocessor performance by having the application code located in internal block RAMs, speedups from 1.4x to 2.8x were estimated.

Abstract