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Leakage power is a major concern in current and future microprocessor designs. In this paper, we explore the potential of architectural techniques to reduce leakage through power-gating of execution units. This paper first develops parameterized analytical equations that estimate the break-even point for application of power-gating techniques. The potential for power gating execution units is then evaluated, for the range of relevant break-even points determined by the analytical equations, using a state-of-the-art out-of-order superscalar processor model. The power gating potential of the floating-point and fixed-point units of this processor is then evaluated using three different techniques to detect opportunities for entering sleep mode; ideal, time-based, and branch-misprediction-guided. Our results show that using the time-based approach, floating-point units can be put to sleep for up to 28% of the execution cycles at a performance loss of 2%. For the more difficult to power-gate fixed-point units, the branch misprediction guided technique allows the fixed-point units to be put to sleep for up to 40% more of the execution cycles compared to the simpler time-based technique, with similar performance impact. Overall, our experiments demonstrate that architectural techniques can be used effectively in power-gating execution units.

Abstract

While Processing-in-Memory has been investigated for decades, it has not been embraced commercially. A number of emerging technologies have renewed interest in this topic. In particular, the emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube device have made it more practical to move computation near memory. However, the literature is missing a detailed analysis of a killer application that can leverage a Near Data Computing (NDC) architecture. This paper focuses on in-memory MapReduce workloads that are commercially important and are especially suitable for NDC because of their embarrassing parallelism and largely localized memory accesses. The NDC architecture incorporates several simple processing cores on a separate, non-memory die in a 3D-stacked memory package; these cores can perform Map operations with efficient memory access and without hitting the bandwidth wall. This paper describes and evaluates a number of key elements necessary in realizing efficient NDC operation: (i) low-EPI cores, (ii) long daisy chains of memory devices, (iii) the dynamic activation of cores and SerDes links. Compared to a baseline that is heavily optimized for MapReduce execution, the NDC design yields up to 15X reduction in execution time and 18X reduction in system energy.

Abstract