Targeting static and dynamic workloads with a reconfigurable VLIW processor

Embedded systems range from very simple devices, such as a digital watch, to highly complex systems such as smartphones. In these complex devices, an increasing number of applications need to be executed on a computing platform. Moreover, the number of applications (or programs) usually exceeds the number of processors found on such platforms. This creates the need for scheduling. Furthermore, each program exhibits different characteristics and their interaction with the (real-life) environmentment leads to real-time requirements. Consequently, the set of programs, called workload, exhibits highly dynamic behavior. Workloads can be dynamic in intensity (i.e., the number of concurrent tasks), characteristics (amount and type of parallelism), and requirements (real-time constraints, power budgets, performance). We argue that dynamic workloads require a dynamic computing platform and propose to use one that comprises the 휌-VEX reconfigurable VLIW processor. It can dynamically adapt to the workload while it is running. Adaptations can be triggered by a user, programmer, compiler, or an operating system. The latter two methods can operate fully automatic and exploring these is one of the goals of this work. Besides dynamic workloads, a number of new classes of embedded devices are running application programs that are very static, but require very high throughput. Examples are the latest generations mobile telecommunications hardware and vision-based applications (automation, surveillance, automated driving). In this case, adapting to the workload at run-time is not advantageous because there are no changes to adapt to. Optimizing for these applications is possible, but must be done before the hardware platform is manufactured (during the design phase) or by making use of Field-Programmable Gate Arrays (FPGAs). This thesis explores the use of the proposed reconfigurable processor to target the full spectrum of embedded workloads. First, design-time reconfigurability is employed to optimize a hardware platform for a static, streaming image processing workload. Second, we explore the run-time reconfigurable processor for dynamic workloads. This is achieved by adapting to a single program to optimize energy efficiency, followed by adapting to a generated set of programs optimizing for throughput. Third, the real-time characteristics of the processor are evaluated and it is shown to have better schedulability compared to static processors. The VLIW architecture results in good timing-predictability, which allows finding tight bounds on the worst-case execution time. Last, we show that the processor is able to assign more parallel execution resources to a static program that is added into the workload, while still guaranteeing time-safety for critical tasks