LoCool: Fighting Hot Spots Locally for Improving System Energy Efficiency

Kaplan, Fulya; Said, Mostafa; Reda, Sherief; Coskun, Ayse K.

doi:10.1109/tcad.2019.2902355

Cited by 10 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the improvement of the range of heat dissipation solutions that can be simulated received far more limited attention, especially when considering transient simulation support. Among works which are the nearest to our design goals, we can cite LoCool [9], a recent thermal model which extends HotSpot with a compact model for on-chip liquid cooling similar to the 3D-ICE one, as well as a linearized ThermoElectric Cooler (TEC) model to model localized hotspot cooling. Although this simulator adds new cooling capabilities, its monolithic architecture still does not make it open to extensibility towards generic cooling methods.…”

Section: Related Workmentioning

confidence: 99%

“…where φ is the heat flux [W/m 2 ] leaving the solid and (x, y, z) a point on its surface, so that (3) provides a boundary condition [13] RK4 3D-ICE [12] Euler Basic ISAC [18] RK4 Basic LUTSim [19] n/a 1 Basic Yu et al [20] n/a 1 Ladenheim et al [21] Krylov LoCool [9] n/a 1 Therminator [22] n/a 1 NanoHeat [23] NHAR MTA [14] Krylov This work Euler for (1): on any finite area S : {f s (x, y, z) = 0 ∩ (x, y, z) ∈ Ξ} of the solid boundary surface, Ξ being a convenient compact set in R 3 , the leaving power…”

Section: A Modeling Of Heating Solids With Conventional Dissipationmentioning

confidence: 99%

“…A second one is now taking place as liquid cooling makes its way in high-end desktop PCs [4] and datacenters, where it is rapidly gaining importance [5]. Other cooling technologies on the horizon are evaporative cooling [6], combined cooling and on-chip power generation [7], thermoelectric cooling [8] and more [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

Terraneo

Leva

Fornaciari

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

The increasing power density in modern highperformance multi-processor system-on-chip (MPSoC) is fueling a revolution in thermal management. On the one hand, thermal phenomena are becoming a critical concern, making accurate and efficient simulation a necessity. On the other hand, a variety of physically heterogeneous solutions are coming into play: liquid, evaporative, thermoelectric cooling, and more. A new generation of simulators, with unprecedented flexibility, is thus required. In this paper, we present 3D-ICE 3.0, the first thermal simulator to allow for accurate nonlinear descriptions of complex and physically heterogeneous heat dissipation systems, while preserving the efficiency of latest compact modeling frameworks at the silicon die level. 3D-ICE 3.0 allows designers to extend the thermal simulator with new heat sink models while simplifying the time-consuming step of model validation. Support for nonlinear dynamic models is included, for instance to accurately represent variable coolant flows. Our results present validated models of a commercial water heat sink and an air heat sink plus fan that achieve an average error below 1 • C and simulate, respectively, up to 3x and 12x faster than the real physical phenomena.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: A Modeling Of Heating Solids With Conventional Dissipationmentioning

confidence: 99%

See 1 more Smart Citation

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

Terraneo

Leva

Fornaciari

et al. 2022

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

show abstract

Experimental investigation of microchannel heat sink performance under non-uniform heat load conditions with different flow configurations

Shanmugam,

Maganti

2024

International Journal of Thermal Sciences

View full text Add to dashboard Cite

Neural network-based cooling design for high-performance processors

Yuan

Coskun

2022

iScience

Self Cite

View full text Add to dashboard Cite

Summary Ultra-high chip power densities that are expected to surpass 1-2kW/cm 2 in future high-performance systems cannot be easily handled by conventional cooling methods. Various emerging cooling methods, such as liquid cooling via microchannels, thermoelectric coolers (TECs), two-phase vapor chambers, and hybrid cooling options have been designed to efficiently remove heat from high-performance processors. However, selecting the optimal cooling solution for a given chip and determining the optimal cooling parameters for that solution to achieve high efficiency are open problems. These problems are, in fact, computationally expensive because of the massive space of possible solutions. To address this design challenge, this article introduces a deep learning-based cooling design optimization flow that rapidly and accurately converges to the optimal cooling solution as well as the optimal cooling parameters for a given chip floorplan and its power profile.

show abstract

LoCool: Fighting Hot Spots Locally for Improving System Energy Efficiency

Cited by 10 publications

References 33 publications

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

3D-ICE 3.0: Efficient Nonlinear MPSoC Thermal Simulation With Pluggable Heat Sink Models

Experimental investigation of microchannel heat sink performance under non-uniform heat load conditions with different flow configurations

Neural network-based cooling design for high-performance processors

Contact Info

Product

Resources

About