Performance Analysis of Application-Specific Multicore Systems on Chip

Sammanfattning: The last two decades have witnessed the birth of revolutionary technologies in data communications including wireless technologies, System on Chip (SoC), Multi Processor SoC (MPSoC), Network on Chip (NoC), and more. At the same time we have witnessed that performance does not always keep pace with expectations in many services like multimediaservices and biomedical applications. Moreover, the IT market has suffered from some crashes. Hence, this triggered us to think of making use of available technologies and developing new ones so that the performance level is suitable for given applications and services. In the medical field, from a statistical viewpoint, the biggest diseases in number of deaths are heart diseases, namely Cardiovascular Disease (CVD) and Stroke. The application with the largest market for CVD is the electrocardiogram (ECG/EKG) analysis. According to the World Health Organization (WHO) report in 2003, 29.2% of global deaths are due to CVD and Stroke, half of which could be prevented if there was proper monitoring. We found in the new advance in microelectronics, NoC, SoC, and MPSoC, a chance of a solution for such a big problem. We look at the communication technologies, wireless networks, and MPSoC and realize that many projects can be founded, and they may affect people's lives positively, as for example, curing people more rapidly, as well as homecare of such large scale diseases. These projects have a medical impact as well as economic and social impacts. The intention is to use performance analysis of interconnected microelectronic systems and combine it with MPSoC and NoC technologies in order to evolve to new systems on chip that may make a difference. Technically, we aim at rendering more computations in less time, on a chip with smaller volume, and with less expense. The performance demand and the vision of having a market success, i.e. contributing to lower healthcare costs, pose many challenges on the hardware/software co-design to meet these goals. This calls upon the development of new integrated circuits featuring increased energy efficiency while providing higher computation capabilities, i.e. better performance. The biomedical application of ECG analysis is an ideal target for an application-specific SoC implementation. However, new 12-lead ECG analyses algorithms are needed to meet the aforementioned goals. In this thesis, we present two novel algorithms for ECG analysis, namely the Autocorrelation-Function (ACF) based algorithm and the Fast Fourier Transform (FFT) based algorithm. In this respect, we explore the design space by analyzing different hardware and software architectures. As a result, we realize a design with twelve processors that can compute 3.5 million arithmetic computations and respect the real time hard deadline for our biomedical application (3.5-4seconds), and that can deploy the ACF-based and FFT-based algorithms. Then, we investigate the configuration space looking for the most effective solution, performance and energy-wise. Consequently, we present three interconnect architectures (Single Bus, Full Crossbar, and Partial Crossbar) and compare them with existing solutions. The sampling frequencies of 2.2 KHz and 4 KHz, with 12 DSPs, are found to be the critical points for our Shared-Bus design and Crossbar architecture, respectively. We also show how our performance analysis methods can be applied to such a field of SoC design and with a specific purpose application in order to converge to a solution that is acceptable from a performance viewpoint, meets the real-time demands, and can be implemented with the present technologies while at the same time paving the way for easier and faster development. In order to connect our MPSoC solution to communication networks to transmit the medical results to a healthcare center, we come up with new protocols that will allow the integration of multiple networks on chips in a communication network. Finally, we present a methodology for HW/SW Codesign for application-specific systems (with focus on biomedical applications) that require a large number of computations since this will foster the convergence to solutions that are acceptable from a performance point of view.