Reconfigurable processors with fine-grained runtime-reconfigurable fabrics are used to speed up applications from different domains. Such a reconfigurable fabric allows loading of application-specific accelerators, where multiple accelerators can be combined using a coarse-grained runtimereconfigurable μProgram to speed up complex computationally intensive kernels. To allow a large degree of adaptivity in the reconfigurable fabric, as it is required by, e.g., multitasking systems, the μProgram for a kernel should not be generated at compile time, as it would constrain the adaptivity of the system. To enable flexible and efficient use of the reconfigurable fabric, we propose the necessary algorithms for runtime: 1) accelerator placement (i.e., deciding where on the fabric an accelerator should be reconfigured at runtime); 2) μProgram generation; and 3) μProgram caching. Accelerator synthesis and implementation are done at compile time to reduce runtime overhead in generating accelerators. We evaluate the proposed algorithms using different application scenarios and demonstrate the proposed concepts on an field-programmable gate array-based prototype of a reconfigurable processor. In comparison with state-of-the-art reconfigurable processors that generate μPrograms at compile time, we obtain an average speedup of 1.29× (up to 1.84×).

Application-specific accelerators may provide considerable speedup in single-core systems with a runtime-reconfigurable fabric (for simplicity called "fabric" in the following). A reconfigurable core, i.e. processor core pipeline coupled to a fabric, can be integrated along with regular general purpose processor cores (GPPs) into a reconfigurable multi-core system with widely improved system performance. As most applications only use a fraction of the available fabric at a time, making the fabric usable by the GPPs (in addition to the reconfigurable core) in such a multi-core system is desirable. Existing work focused on algorithms that decide the amount of fabric that is assigned to each core in a multi-core system. However, when multiple cores access the fabric simultaneously, they are either limited to serialized fabric access or, when parallel access is supported, the size of the fabric share assigned to a core is inflexible and tends to be over- or undersized for the running application, thereby not efficiently utilizing the fabric. We propose a novel approach that allows GPPs to access the fabric of the reconfigurable core and that enables concurrent fabric utilization on-the-fly through merging fabric accesses from different cores at run-time. Compared to state-of-the art, our approach improves performance of the GPPs in a reconfigurable multi-core system by 1.3x on average, without reducing the performance of the reconfigurable core.

Processors with an embedded runtime reconfigurable fabric have been explored in academia and industry started production of commercial platforms (e.g. Xilinx Zynq-7000). While providing significant performance and efficiency, the comparatively long reconfiguration time limits these advantages when applications request reconfigurations frequently. In multi-tasking systems frequent task switches lead to frequent reconfigurations and thus are a major hurdle for further performance increases. Sophisticated task scheduling is a very effective means to reduce the negative impact of these reconfiguration requests. In this paper, we propose an online approach for combined task scheduling and re-distribution of reconfigurable fabric between tasks in order to reduce the makespan, i.e. the completion time of a taskset that executes on a runtime reconfigurable processor. Evaluating multiple tasksets comprised of multimedia applications, our proposed approach achieves makespans that are on average only 2.8% worse than those achieved by a theoretical optimal scheduling that assumes zero-overhead reconfiguration time. In comparison, scheduling approaches deployed in state-of-the-art reconfigurable processors achieve makespans 14%-20% worse than optimal. As our approach is a purely software-side mechanism, a multitude of reconfigurable platforms aimed at multi-tasking can benefit from it.

Register allocation is one of the most time-consuming parts of the compilation process. Depending on the quality of the register allocation, a large amount of shuffle code to move values between registers is generated. In this paper, we propose a processor architecture extension to provide register file permutations by which the shuffle code can be implemented more efficiently. We present compiler support to utilize this extension, an evaluation regarding performance and compilation time using the SPEC CINT2000 benchmark, as well as an analysis of area and frequency overhead of our architecture implementation. We find that using our extension, the number of executed instructions is reduced by up to 5.1 % while the compilation time is unaffected.

This paper introduces a scalable hardware and software platform applicable for demonstrating the benefits of the invasive computing paradigm. The hardware architecture consists of a heterogeneous, tile-based manycore structure while the software architecture comprises a multi-agent management layer underpinned by distributed runtime and OS services. The necessity for invasive-specific hardware assist functions is analytically shown and their integration into the overall manycore environment is described.

Processor architectures with Fine-Grained Reconfigurable Accelerators (FGRAs) allow for a high degree of adaptivity to address varying application requirements. When processing computation intensive kernels, multiple FGRAs may be used to execute a complex function. In order to exploit the adaptivity of a fine-grained reconfigurable fabric, a runtime system should decide when and which FGRAs to reconfigure with respect to application requirements. To enable this adaptivity, a flexible infrastructure is required that allows combining FGRAs to execute complex functions. We propose a mixed-grained reconfigurable architecture composed from a Coarse-Grained Reconfigurable Infrastructure (CGRI) that connects the FGRAs. At runtime we synthesize CGRI configurations that depend on decisions of the runtime system, e.g. which FGRAs shall be reconfigured. Synthesis and place & route of the FGRAs are done at compile time for performance reasons. Combined, this results in a partial online synthesis for mixed grained reconfigurable architectures, which allows maintaining a low runtime overhead while exploiting the inherent adaptivity of the reconfigurable fabric. In this work we focus on the crucial parts of synthesizing the configurations for the CGRI at runtime, propose algorithms, and compare their performance/overhead trade-offs for different application scenarios. We are the first to exploit the increased adaptivity of FGRAs that are connected by a CGRI, by using our partial online synthesis. In comparison to a state-of-the-art reconfigurable architecture that synthesizes the configurations for the CGRI at compile time we obtain an average speedup of 1.79x.

Multi-tasking is one of the main requirements for complex embedded systems to fulfill user expectations (e.g. flexibility of the system), increase the resource utilization, and thus increase the system efficiency. In general, the flexibility and efficiency can be increased by incorporating a fine-grained reconfigurable fabric (e.g. an embedded FPGA) that is coupled with a general-purpose processor and accelerates the computationally intensive kernels. This work focuses on reconfigurable processors that use a reconfigurable fabric to implement Special Instructions (SIs) that are invoked by the processor and process data-dominant parts. For each SI the decision whether it is executed in hardware or emulated in software can be changed dynamically at runtime. In this paper, we present our novel Performance Aware Task Scheduler (PATS) that decides the task schedule at runtime while considering the specific system state of the reconfigurable processor. For instance, if a task t has to emulate several SI executions in software because reconfiguring the corresponding hardware implementations is not completed yet, then it might be more efficient to schedule other tasks first, depending on the soft-deadlines of the tasks, until the reconfigurations of that task t are completed. In comparison to other task schedulers (earliest deadline first, rate monotonic scheduling, and round robin), PATS achieves on average a 1.45x better system tardiness (i.e., the sum of cycles by which tasks miss their deadlines). Additionally, PATS reduces the make span (i.e. the time when all tasks have completed all of their jobs) on average by 1.17x (up to 1.58x). Especially in challenging multi-tasking scenarios with tight deadlines or a small reconfigurable fabric PATS performs significantly better than other task schedulers do.