000181560 001__ 181560
000181560 005__ 20190331192702.0
000181560 0247_ $$2doi$$a10.1145/2390191.2390197
000181560 022__ $$a1084-4309
000181560 02470 $$2ISI$$a000313663500006
000181560 037__ $$aARTICLE
000181560 245__ $$aOnline Thermal Control Methods for Multi-Processor Systems
000181560 269__ $$a2012
000181560 260__ $$bAssociation for Computing Machinery$$c2012$$aNew York
000181560 300__ $$a26
000181560 336__ $$aJournal Articles
000181560 520__ $$aWith technological advances, the number of cores integrated on a chip is increasing. This, in turn is leading to thermal constraints and thermal design challenges. Temperature gradients and hot-spots not only affect the performance of the system, but also lead to unreliable circuit operation and affect the life-time of the chip. Meeting temperature constraints and reducing hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this article we analyze the use of four of the most promising families of online control techniques for thermal management of multi-processors system-on-chip (MPSoC). In particular, in our exploration we aim at achieving an online smooth thermal control action that minimizes the performance loss as well as the computational and hardware overhead of embedding a thermal management system inside the MPSoC. The definition of the optimization problem to tackle in this work considers the thermal profile of the system, its evolution over time and current time-varying workload requirements. Thus, this problem is formulated as a finite-horizon optimal control problem and we analyze the control features of different on-line thermal control approaches. In addition, we implemented the policies on an MPSoC hardware simulation platform and performed experiments on a cycle-accurate model of the 8-core Niagara multi-core architecture using benchmarks ranging from web-accessing to playing multimedia. Results show different trade-offs among the analyzed techniques regarding the thermal profile, the frequency setting, the power consumption and the implementation complexity.
000181560 6531_ $$aMPSoC
000181560 6531_ $$athermal management
000181560 6531_ $$aDVFS
000181560 6531_ $$apower management
000181560 6531_ $$amodel predictive control
000181560 6531_ $$aconvex optimization
000181560 6531_ $$ahot spots
000181560 6531_ $$aembedded systems
000181560 700__ $$0242412$$g178989$$aZanini, Francesco
000181560 700__ $$0240268$$g169199$$aAtienza Alonso, David
000181560 700__ $$0246471$$g207237$$aJones, Colin
000181560 700__ $$g171049$$aBenini, Luca$$0243773
000181560 700__ $$0240269$$g167918$$aDe Micheli, Giovanni
000181560 773__ $$j18$$tACM Transactions on Design Automation of Electronic Systems$$k1$$q6:1-6:26
000181560 8564_ $$uhttps://infoscience.epfl.ch/record/181560/files/a6-zanini.pdf$$zn/a$$s599315$$yn/a
000181560 909C0 $$xU11140$$0252283$$pLSI1
000181560 909C0 $$0252053$$pLA
000181560 909C0 $$xU11977$$0252050$$pESL
000181560 909CO $$qGLOBAL_SET$$pSTI$$pIC$$particle$$ooai:infoscience.tind.io:181560
000181560 917Z8 $$x169199
000181560 917Z8 $$x112915
000181560 917Z8 $$x112915
000181560 937__ $$aEPFL-ARTICLE-181560
000181560 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000181560 980__ $$aARTICLE