Exploiting Dark Silicon for Energy Efficiency Nikos Hardavellas Northwestern University, EECS.

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Presentation transcript:

Exploiting Dark Silicon for Energy Efficiency Nikos Hardavellas Northwestern University, EECS

© Nikos Hardavellas 2 NSF SEEDM Workshop, May 2011 Technology Scaling Changes the HW Landscape Transistor counts increase exponentially, but… Traditional HW power-aware techniques inadequate (e.g., voltage-freq. scaling) Dark Silicon !!! Can no longer power the entire chip (voltages, cooling do not scale) [Watanabe et al., ISCA’10]

© Nikos Hardavellas 3 NSF SEEDM Workshop, May 2011 The Rise of Dark Silicon Exponentially-large area is left unutilized (dark). Should we waste it?

© Nikos Hardavellas 4 NSF SEEDM Workshop, May 2011 Design for Dark Silicon Don’t waste it; harness it instead!  Use dark silicon to implement specialized cores Applications cherry-pick few cores, rest of chip is powered off Vast unused area  many cores  likely to find good matches

© Nikos Hardavellas 5 NSF SEEDM Workshop, May x LOWER ENERGY compared to best conventional alternative First-Order Core Specialization Model Modeling of physically-constrained CMPs across technologies Model of specialized cores based on ASIC implementation of H.264:  Implementations on custom HW (ASICs), FPGAs, multicores (CMP)  Wide range of computational motifs, extensively studied Frames per sec Energy per frame (mJ) Performance gap of CMP vs. ASIC Energy gap of CMP vs. ASIC ASIC304 CMP IME x707x FME x468x Intra x157x CABAC x261x [Hameed et al., ISCA’10]

© Nikos Hardavellas 6 NSF SEEDM Workshop, May 2011 Research Directions Which candidates are best for off-loading to specialized cores? What should these cores look like?  Exploit commonalities to avoid core over-specialization  Impact on ISA? What are the appropriate language/compiler/runtime techniques to drive execution migration?  Impact on scheduler? How to restructure software/algorithms for heterogeneity?

© Nikos Hardavellas 7 NSF SEEDM Workshop, May 2011 Thank You! Questions? References: N. Hardavellas, M. Ferdman, B. Falsafi, and A. Ailamaki. Toward Dark Silicon in Servers. IEEE Micro, Vol. 31, No. 4, July/August N. Hardavellas. Chip multiprocessors for server workloads. PhD thesis, Carnegie Mellon University, Dept. of Computer Science, August N. Hardavellas, M. Ferdman, A. Ailamaki, and B. Falsafi. Power scaling: the ultimate obstacle to 1K-core chips. Technical Report NWU-EECS-10-05, Northwestern University, Evanston, IL, March R. Hameed, W. Qadeer, M. Wachs, O. Azizi, A. Solomatnikov, B. C. Lee, S. Richardson, C. Kozyrakis, and M. Horowitz. Understanding sources of inefficiency in general-purpose chips. In Proc. of ISCA, June E. S. Chung, P. A. Milder, J. C. Hoe, and K. Mai. Single-chip heterogeneous computing: does the future include custom logic, FPGAs, and GPGPUs? In Proc. of MICRO, December 2010.