A soft multi-core architecture for edge detection and data analysis of microarray images

As configurable processing advances, elements from the traditional approaches of both hardware and software development can be combined by incorporating customized, application-specific computational resources into the processor’s architecture, especially in the case of field-programmable-gate-array-based systems with soft-processors, so as to enhance the performance of embedded applications. This paper explores the use of several different microarchitectural alternatives to increase the performance of edge detection algorithms, which are of fundamental importance for the analysis of DNA microarray images. Optimized application-specific hardware modules are combined with efficient parallelized software in an embedded soft-core-based multi-processor. It is demonstrated that the performance of one common edge detection algorithm, namely Sobel, can be boosted remarkably. By exploiting the architectural extensions offered by the soft-processor, in conjunction with the execution of carefully selected application-specific instruction-set extensions on a custom-made accelerating co-processor connected to the processor core, we introduce a new approach that makes this methodology noticeably more efficient across various applications from the same domain, which are often similar in structure. With flexibility to update the processing algorithms, an improvement reaching one order of magnitude over all-software solutions could be obtained. In support of this flexibility, an effective adaptation of this approach is demonstrated which performs real-time analysis of extracted microarray data; the proposed reconfigurable multi-core prototype has been exploited with minor changes to achieve almost 5× speedup.