Recent trends in technology scaling have shifted the processing paradigm to multicores. Depending on the characteristics of the cores, the multicores can be either symmetric or asymmetric. Prior research has shown that Asymmetric Multicore Processors (AMPs) outperform their symmetric (SMP) counterparts within a given resource and power budget. But, due to the heterogeneity in core-types and time-varying workload behavior, thread-to-core assignment is always a challenge in AMPs. As the computational requirements vary significantly across different applications and with time, there is a need to dynamically allocate appropriate computational resources on demand to suit the applications’ current needs, in order to maximize the performance and minimize the energy consumption. Performance/power of the applications could be further increased by dynamically adapting the voltage and frequency of the cores to better fit the changing characteristics of the workloads. Not only can a core be forced to a low power mode when its activity level is low, but the power saved by doing so could be opportunistically re-budgeted to the other cores to boost the overall system throughput. To this end, we propose a novel solution that seamlessly combines heterogeneity with a Dynamic Reconfiguration Framework (DRF). The proposed dynamic reconfiguration framework is equipped with Dynamic Resource Allocation (DRA) and Voltage/Frequency Adaptation (DVFA) capabilities to adapt the core resources and operating conditions at runtime to the changing demands of the applications. As a proof of concept, we illustrate our proposed approach using a dual-core AMP and demonstrate significant performance/power benefits over various baselines.

The semiconductor industry has been driven by Moore's law for almost half a century. Miniaturization of device size has allowed more transistors to be packed into a smaller area while the improved transistor performance has resulted in a significant increase in frequency. Increased density of devices and rising frequency led, unfortunately, to a power density problem which became an obstacle to further integration. The processor industry responded to this problem by lowering processor frequency and integrating multiple processor cores on a die, choosing to focus on Thread Level Parallelism (TLP) for performance instead of traditional Instruction Level Parallelism (ILP). While continued scaling of devices have provided unprecedented integration, it has also unfortunately led to a few serious problems: The first problem is that of increasing rates of system failures due to soft errors and aging defects. Soft errors are caused by ionizing radiations that originate from radioactive contaminants or secondary release of charged particles from cosmic neutrons. Ionizing radiations may charge/discharge a storage node causing bit flips which may result in a system failure. In this dissertation, we propose solutions for online detection of such errors in microprocessors. A small and functionally limited core called the Sentry Core (SC) is added to the multicore. It monitors operation of the functional cores in the multicore and whenever deemed necessary, it opportunistically initiates Dual Modular redundancy (DMR) to test the operation of the cores in the multicore. This scheme thus allows detection of potential core failure and comes at a small hardware overhead. In addition to detection of soft errors, this solution is also capable of detecting errors introduced by device aging that results in failure of operation. The solution is further extended to verify cache coherence transactions. A second problem we address in this dissertation relate to power concerns. While the multicore solution addresses the power density problem, overall power dissipation is still limited by packaging and cooling technologies. This limits the number of cores that can be integrated for a given package specification. One way to improve performance within this constraint is to reduce power dissipation of individual cores without sacrificing system performance. There have been prior solutions to achieve this objective that involve Dynamic Voltage and Frequency Scaling (DVFS) and the use of sleep states. DVFS and sleep states take advantage of coarse grain variation in demand for computation. In this dissertation, we propose techniques to maximize performance-per-power of multicores at a fine grained time scale. We propose multiple alternative architectures to attain this goal. One of such architectures we explore is Asymmetric Multicore Processors (AMPs). AMPs have been shown to outperform the symmetric ones in terms of performance and Performance-per-Watt for a fixed resource and power budget. However, effectiveness of these architectures depends on accurate thread-to-core scheduling. To address this problem, we propose online thread scheduling solutions responding to changing computational requirements of the threads. Another solution we consider is for Symmetric Multicore processors (SMPs). Here we target sharing of the large and underutilized resources between pairs of cores. While such architectures have been explored in the past, the evaluations were incomplete. Due to sharing, sometimes the shared resource is a bottleneck resulting in significant performance loss. To mitigate such loss, we propose the Dynamic Voltage and Frequency Boosting (DVFB) of the shared resources. This solution is found to significantly mitigate performance loss in times of contention. We also explore in this dissertation, performance-per-Watt improvement of individual cores in a multicore. This is based on dynamic reconfiguration of individual cores to run them alternately in out-of-order (OOO) and in-order (InO) modes adapting dynamically to workload characteristics. This solution is found to significantly improve power efficiency without compromising overall performance. Thus, in this dissertation we propose solutions for several important problems to facilitate continued scaling of processors. Specifically, we address challenges in the area of reliability of computation and propose low power design solutions to address power constraints.