E³ : energy-efficient EDGE architectures

textIncreasing power dissipation is one of the most serious challenges facing designers in the microprocessor industry. Power dissipation, increasing wire delays, and increasing design complexity have forced industry to embrace multi-core architectures or chip multiprocessors (CMPs). While CMPs mitigate wire delays and design complexity, they do not directly address single-threaded performance. Additionally, programs must be parallelized, either manually or automatically, to fully exploit the performance of CMPs. Researchers have recently proposed an architecture called Explicit Data Graph Execution (EDGE) as an alternative to conventional CMPs. EDGE architectures are designed to be technology-scalable and to provide good single-threaded performance as well as exploit other types of parallelism including data-level and thread-level parallelism. In this dissertation, we examine the energy efficiency of a specific EDGE architecture called TRIPS Instruction Set Architecture (ISA) and two microarchitectures called TRIPS and TFlex that implement the TRIPS ISA. TRIPS microarchitecture is a first-generation design that proves the feasibility of the TRIPS ISA and distributed tiled microarchitectures. The second-generation TFlex microarchitecture addresses key inefficiencies of the TRIPS microarchitecture by matching the resource needs of applications to a composable hardware substrate. First, we perform a thorough power analysis of the TRIPS microarchitecture. We describe how we develop architectural power models for TRIPS. We then improve power-modeling accuracy using hardware power measurements on the TRIPS prototype combined with detailed Register Transfer Level (RTL) power models from the TRIPS design. Using these refined architectural power models and normalized power modeling methodologies, we perform a detailed performance and power comparison of the TRIPS microarchitecture with two different processors: 1) a low-end processor designed for power efficiency (ARM/XScale) and 2) a high-end superscalar processor designed for high performance (a variant of Power4). This detailed power analysis provides key insights into the advantages and disadvantages of the TRIPS ISA and microarchitecture compared to processors on either end of the performance-power spectrum. Our results indicate that the TRIPS microarchitecture achieves 11.7 times better energy efficiency compared to ARM, and approximately 12% better energy efficiency than Power4, in terms of the Energy-Delay-Squared (ED²) metric. Second, we evaluate the energy efficiency of the TFlex microarchitecture in comparison to TRIPS, ARM, and Power4. TFlex belongs to a class of microarchitectures called Composable Lightweight Processors (CLPs). CLPs are distributed microarchitectures designed with simple cores and are highly configurable at runtime to adapt to resource needs of applications. We develop power models for the TFlex microarchitecture based on the validated TRIPS power models. Our quantitative results indicate that by better matching execution resources to the needs of applications, the composable TFlex system can operate in both regimes of low power (similar to ARM) and high performance (similar to Power4). We also show that the composability feature of TFlex achieves a signification improvement (2 times) in the ED² metric compared to TRIPS. Third, using TFlex as our experimental platform, we examine the efficacy of processor composability as a potential performance-power trade-off mechanism. Most modern processors support a form of dynamic voltage and frequency scaling (DVFS) as a performance-power trade-off mechanism. Since the rate of voltage scaling has slowed significantly in recent process technologies, processor designers are in dire need of alternatives to DVFS. In this dissertation, we explore processor composability as an architectural alternative to DVFS. Through experimental results we show that processor composability achieves almost as good performance-power trade-offs as pure frequency scaling (no changes in supply voltages), and a much better performance-power trade-off compared to voltage and frequency scaling (both supply voltage and frequency change). Next, we explore the effects of additional performance-improving techniques for the TFlex system on its energy efficiency. Researchers have proposed a variety of techniques for improving the performance of the TFlex system. These include: (1) block mapping techniques to trade off intra-block concurrency with communication across the operand network; (2) predicate prediction and (3) operand multi-cast/broadcast mechanism. We examine each of these mechanisms in terms of its effect on the energy efficiency of TFlex, and our experimental results demonstrate the effects of operand communication, and speculation on the energy efficiency of TFlex. Finally, this dissertation evaluates a set of fine-grained power management (FGPM) policies for TFlex: instruction criticality and controlled speculation. These policies rely on a temporally and spatially fine-grained dynamic voltage and frequency scaling (DVFS) mechanism for improving power efficiency. The instruction criticality policy seeks to improve power efficiency by mapping critical computation in a program to higher performance-power levels, and by mapping non-critical computation to lower performance-power levels. Controlled speculation policy, on the other hand, maps blocks that are highly likely to be on correct execution path in a program to higher performance levels, and the other blocks to lower performance levels. Our experimental results indicate that idealized instruction criticality and controlled speculation policies improve the operating range and flexibility of the TFlex system. However, when the actual overheads of fine-grained DVFS, especially energy conversion losses of voltage regulator modules (VRMs), are considered the power efficiency advantages of these idealized policies quickly diminish. Our results also indicate that the current conversion efficiencies of on-chip VRMs need to improve to as high as 95% for the realistic policies to be feasible.Computer Science