Effective thermal control techniques for liquid-cooled 3D multi-core processors

Microchannel liquid cooling shows great potential in cooling 3D processors. However, the cooling of 3D processors is limited due to design-time and run-time challenges. Moreover, in new technologies, the processor power density is continually increasing and this will bring more serious challenges to liquid cooling. In this paper, we propose two thermal control techniques: 1) Core Vertically Placed (CVP) technique. According to the architecture of a processor core, two schemes are given for placing a core vertically onto multilayers. The 3D processor with the CVP technique can be better cooled since its separate hotspot blocks have a larger total contact area with the cooler surroundings. 2) Thermoelectric cooling (TEC) technique. We propose to incorporate the TEC technique into the liquid-cooled 3D processor to enhance the cooling of hotspots. Our experiments show the CVP technique reduces the maximum temperature up to 29.58 °C, and 16.64 °C on average compared with the baseline design. Moreover, the TEC technique effectively cools down a hotspot from 96.86 °C to 78.60 °C. © 2013 IEEE

Improving processor efficiency through thermal modeling and runtime management of hybrid cooling strategies

One of the main challenges in building future high performance systems is the ability to maintain safe on-chip temperatures in presence of high power densities. Handling such high power densities necessitates novel cooling solutions that are significantly more efficient than their existing counterparts. A number of advanced cooling methods have been proposed to address the temperature problem in processors. However, tradeoffs exist between performance, cost, and efficiency of those cooling methods, and these tradeoffs depend on the target system properties. Hence, a single cooling solution satisfying optimum conditions for any arbitrary system does not exist. This thesis claims that in order to reach exascale computing, a dramatic improvement in energy efficiency is needed, and achieving this improvement requires a temperature-centric co-design of the cooling and computing subsystems. Such co-design requires detailed system-level thermal modeling, design-time optimization, and runtime management techniques that are aware of the underlying processor architecture and application requirements. To this end, this thesis first proposes compact thermal modeling methods to characterize the complex thermal behavior of cutting-edge cooling solutions, mainly Phase Change Material (PCM)-based cooling, liquid cooling, and thermoelectric cooling (TEC), as well as hybrid designs involving a combination of these. The proposed models are modular and they enable fast and accurate exploration of a large design space. Comparisons against multi-physics simulations and measurements on testbeds validate the accuracy of our models (resulting in less than 1C error on average) and demonstrate significant reductions in simulation time (up to four orders of magnitude shorter simulation times). This thesis then introduces temperature-aware optimization techniques to maximize energy efficiency of a given system as a whole (including computing and cooling energy). The proposed optimization techniques approach the temperature problem from various angles, tackling major sources of inefficiency. One important angle is to understand the application power and performance characteristics and to design management techniques to match them. For workloads that require short bursts of intense parallel computation, we propose using PCM-based cooling in cooperation with a novel Adaptive Sprinting technique. By tracking the PCM state and incorporating this information during runtime decisions, Adaptive Sprinting utilizes the PCM heat storage capability more efficiently, achieving 29\% performance improvement compared to existing sprinting policies. In addition to the application characteristics, high heterogeneity in on-chip heat distribution is an important factor affecting efficiency. Hot spots occur on different locations of the chip with varying intensities; thus, designing a uniform cooling solution to handle worst-case hot spots significantly reduces the cooling efficiency. The hybrid cooling techniques proposed as part of this thesis address this issue by combining the strengths of different cooling methods and localizing the cooling effort over hot spots. Specifically, the thesis introduces LoCool, a cooling system optimizer that minimizes cooling power under temperature constraints for hybrid-cooled systems using TECs and liquid cooling. Finally, the scope of this work is not limited to existing advanced cooling solutions, but it also extends to emerging technologies and their potential benefits and tradeoffs. One such technology is integrated flow cell array, where fuel cells are pumped through microchannels, providing both cooling and on-chip power generation. This thesis explores a broad range of design parameters including maximum chip temperature, leakage power, and generated power for flow cell arrays in order to maximize the benefits of integrating this technology with computing systems. Through thermal modeling and runtime management techniques, and by exploring the design space of emerging cooling solutions, this thesis provides significant improvements in processor energy efficiency.2018-07-09T00:00:00

Conducting polymer nanowires for multi-analyte chemiresistive sensing

A conducting polymer nanowire-based chemiresistive sensor array was developed for the liquid-phase multi-analyte detection. The ability to distinguish and quantify multiple chemical species with a single sensory device can be useful in many areas including food industry, pollution control, biosensors, and explosives detection. A polyaniline nanowire is a good candidate for use as a chemiresistive sensing material due to its large resistivity change and ease of synthesis. However the two most important issues in chemiresistive sensors are the reproducibility in sensing and the selectivity in chemical species. For improving the reproducibility in polyaniline-based chemiresistive sensing, a self-calibration mechanism was proposed. This method utilizes two unique properties of polyaniline: one is the rate of the conductivity decay upon repeated cycling of the electrochemical potential, and the other is the position of the second redox potential, both of which are pH-dependent. These two properties were minimally affected by the polyaniline’s inherent limitations, i.e. hysteresis and degradation, and therefore were effective in obtaining repeatable measurements. In order to enhance the selectivity, a catalyst-based selective detection was proposed. This method is based on the concept that the catalytic reaction between the species and the catalysts causes a local pH change near the polyaniline nanowire network which changes the resistance of the polymer. Finally, a sensor array consisting of polyaniline nanowire-based chemiresistors with each sensing element modified with a unique catalyst was implemented for multi-analyte sensing of ascorbic acid, dopamine, and hydrogen peroxide. Principal component algorithm was applied for the classification and semi-quantification of the chemical species