Increased power densities (and resultant temperatures) and other effects of device scaling are predicted to cause significant lifetime reliability problems in the near future. In this paper, we study two techniques that leverage microarchitectural structural redundancy for lifetime reliability enhancement. First, in structural duplication (SD), redundant microarchitectural structures are added to the processor and designated as spares. Spare structures can be turned on when the original structure fails, increasing the processor's lifetime. Second, graceful performance degradation (GPD) is a technique which exploits existing microarchitectural redundancy for reliability. Redundant structures that fail are shut down while still maintaining functionality, thereby increasing the processor's lifetime, but at a lower performance. Our analysis shows that exploiting structural redundancy can provide significant reliability benefits, and we present guidelines for efficient usage of these techniques by identifying situations where each is more beneficial. We show that GPD is the superior technique when only limited performance or cost resources can be sacrificed for reliability. Specifically, on average for our systems and applications, GPD increased processor reliability to 1.42 times the base value for less than a 5% loss in performance. On the other hand, for systems where reliability is more important than performance or cost, SD is more beneficial. SD increases reliability to 3.17 times the base value for 2.25 times the base cost, for our applications. Finally, a combination of the two techniques (SD+GPD) provides the highest reliability benefit.

The relentless scaling of CMOS technology has provided a steady increase in processor performance for the past three decades. However, increased power densities (hence temperatures) and other scaling effects have an adverse impact on long-term processor lifetime reliability. This paper represents a first attempt at quantifying the impact of scaling on lifetime reliability due to intrinsic hard errors, taking workload characteristics into consideration. For our quantitative evaluation, we use RAMP (Srinivasan et al., 2004), a previously proposed industrial-strength model that provides reliability estimates for a workload, but for a given technology. We extend RAMP by adding scaling specific parameters to enable workload-dependent lifetime reliability evaluation at different technologies. We show that (1) scaling has a significant impact on processor hard failure rates - on average, with SPEC benchmarks, we find the failure rate of a scaled 65nm processor to be 316% higher than a similarly pipelined 180nm processor; (2) time-dependent dielectric breakdown and electromigration have the largest increases; and (3) with scaling, the difference in reliability from running at worst-case vs. typical workload operating conditions increases significantly, as does the difference from running different workloads. Our results imply that leveraging a single microarchitecture design for multiple remaps across a few technology generations will become increasingly difficult, and motivate a need for workload specific, microarchitectural lifetime reliability awareness at an early design stage.

Ensuring long processor lifetimes by limiting failures due to wear-out related hard errors is a critical requirement for all microprocessor manufacturers. We observe that continuous device scaling and increasing temperatures are making lifetime reliability targets even harder to meet. However, current methodologies for qualifying lifetime reliability are overly conservative since they assume worst-case operating conditions. This paper makes the case that the continued use of such methodologies will significantly and unnecessarily constrain performance. Instead, lifetime reliability awareness at the microarchitectural design stage can mitigate this problem, by designing processors that dynamically adapt in response to the observed usage to meet a reliability target. We make two specific contributions. First, we describe an architecture-level model and its implementation, called RAMP, that can dynamically track lifetime reliability, responding to changes in application behavior. RAMP is based on state-of-the-art device models for different wear-out mechanisms. Second, we propose dynamic reliability management (DRM) - a technique where the processor can respond to changing application behavior to maintain its lifetime reliability target. In contrast to current worst-case behavior based reliability qualification methodologies, DRM allows processors to be qualified for reliability at lower (but more likely) operating points than the worst case. Using RAMP, we show that this can save cost and/or improve performance, that dynamic voltage scaling is an effective response technique for DRM, and that dynamic thermal management neither subsumes nor is subsumed by DRM.

Dynamic Thermal Management (DTM) techniques have been proposed to save on thermal packaging and cooling costs for general-purpose processors. However, when invoked, these techniques result in a significant performance degradation. This paper concerns performance-effective DTM for multimedia applications. We make two contributions: (1) Current DTM algorithms are reactive in nature. We propose a predictive DTM algorithm targeted at multimedia applications, which allows the efficient use of response mechanisms that have high invocation overhead. We find that for our applications, our predictive algorithm performs significantly better than existing reactive DTM algorithms. (2) We evaluate the effectiveness of different DTM response mechanisms. Specifically, we demonstrate the importance of tailoring DTM response mechanisms to the thermal "hot-spots" on the chip and the current thermal limit, and show that a predictive combination of architecture adaptation and dynamic voltage scaling (DVS) performs the best across a broad range of applications and thermal limits.

Multimedia applications are an increasingly important workload for general-purpose processors. This paper analyzes frame-level execution time variability for several multimedia applications on general-purpose architectures. There are two reasons for such an analysis. First, it has been conjectured that complex features of such architectures (e.g., out-of-order issue) result in unpredictable execution times, making them unsuitable for meeting real-time requirements of multimedia applications. Our analysis tests this conjecture. Second, such an analysis can be used to effectively employ recently proposed adaptive architectures.

General-purpose processors are expected to be increasingly employed for multimedia workloads on systems where reducing energy consumption is an important goal. Researchers have proposed the use of two forms of hardware adaptation - architectural adaptation and dynamic voltage (and frequency) scaling or DVS - to reduce energy. This paper develops and evaluates an integrated algorithm to control both architectural adaptation and DVS targeted to multimedia applications. It also examines the interaction between the two forms of adaptation, identifying when each will perform better in isolation and when the addition of architectural adaptation will benefit DVS. Our adaptation control algorithm is effective in saving energy and exploits most of the available potential. For the applications and systems studied, DVS is consistently better than architectural adaptation in isolation. The addition of architectural adaptation to DVS benefits some applications, but not all. Finally, in a seemingly counter-intuitive result, we find that while less aggressive architectures reduce energy for fixed frequency hardware, with DVS, more aggressive architectures are often more energy efficient.