Date: Wednesday 21 March 2018
Time: 14:30 - 16:00
Location / Room: Konf. 6
Chair:
Pascal Vivet, CEA-Leti, FR
Co-Chair:
Donghwa Shin, Yeungnam Univ. Daegu, KR
In this session, the first paper presents energy efficiency optimization for CPU-GPU heterogeneous architectures using machine-learning. The next two papers present run-time power modeling and estimation methods for embedded systems. Finally, the last paper presents an online reconfiguration method for photovoltaic power system.
| Time | Label | Presentation Title Authors |
|---|---|---|
| 14:30 | 7.2.1 | AIRAVAT: IMPROVING ENERGY EFFICIENCY OF HETEROGENEOUS APPLICATIONS Speaker: Trinayan Baruah, Northeastern University, US Authors: Trinayan Baruah1, Yifan Sun1, Shi Dong1, David Kaeli1 and Norm Rubin2 1Northeastern University, Boston, US; 2NVIDIA, US Abstract We are seeing an emerging class of applications that attempt to make use of both the CPU and GPU in a heterogeneous system. The peak performance for these applications is achieved when both the CPU and GPU are used collaboratively. However, along with this increased gain in performance, power and energy management is a larger challenge. In this paper we address the issue of executing applications that utilize both the CPU and GPU in an energy efficient way. Towards this end we propose a power management framework named Airavat that tunes the CPU, GPU and memory frequencies, synergestically, in order to improve the energy efficiency of collaborative CPU-GPU applications. Airavat uses machine learning-based prediction models, combined with feedback based Dynamic Voltage and Frequency Scaling to improve the energy efficiency of such applications. We demonstrate our framework on the Jetson TX1 and observe an improvement in terms of Energy Delay Product(EDP) by 24% with only a minimal performance loss. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 15:00 | 7.2.2 | ALL-DIGITAL EMBEDDED METERS FOR ON-LINE POWER ESTIMATION Speaker: Daniele Jahier Pagliari, Politecnico di Torino, IT Authors: Daniele Jahier Pagliari, Valentino Peluso, Yukai Chen, Andrea Calimera, Enrico Macii and Massimo Poncino, Politecnico di Torino, IT Abstract Modern low power designs use multiple knobs for concurrent dynamic and leakage power optimization; supply voltage and threshold voltage are the most adopted. An efficient control of these knobs needs management policies aware of the power breakdown. This implies the availability of smart on-chip strategies for dynamic and leakage power estimation at runtime. In this paper, we address this issue proposing the implementation of embedded dynamic/static power meters that use an optimized regression model fed with data collected from in-situ activity monitors. The number of sensors, their bitwidth and optimal placement are obtained through an automated design flow. The methodology works for general logic and applies not just to processor cores, but also to application-specific designs. We apply our solution to a representative class of benchmarks, showing that it can achieve an average estimation error smaller than 3%, with limited area and power overheads. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 15:30 | 7.2.3 | POWERPROBE: RUN-TIME POWER MODELING THROUGH AUTOMATIC RTL INSTRUMENTATION Speaker: Davide Zoni, Politecnico di Milano, IT Authors: Davide Zoni, Luca Cremona and William Fornaciari, Politecnico di Milano, IT Abstract Online power monitoring represents a de-facto solution to enable energy- and power-aware run-time optimizations for current and future computing architectures. Traditionally, the performance counters of the target architecture are used to feed a software-based, power model that is continuously updated to deliver the required run-time power estimates. The solution introduces a non-negligible performance and energy overhead. Moreover, it is limited to the availability of such performance counters that, however, are not primarily intended for online power monitoring. This paper introduces PowerProbe, a run-time power monitoring methodology that automatically extracts and implements a power model from the RTL description of the target architecture. The solution does not leverage any performance counter to ensure wide applicability. Moreover, the use of ad-hoc hardware that continuously updates the power estimate minimizes both the performance and the power overheads. We employ a fully compliant OpenRisc 1000 implementation to validate PowerProbe. The results highlight an average prediction error within 9% (standard deviation less than 2%), with a power and area overheads limited to 6.89% and 4.71%, respectively. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 15:45 | 7.2.4 | DESIGN OPTIMIZATION OF PHOTOVOLTAIC ARRAY ON A CURVED SURFACE Speaker: Sangyoung Park, Technical University of Munich, DE Authors: Sangyoung Park and Samarjit Chakraborty, Technical University of Munich, DE Abstract Flexible photovoltaic (PV) arrays often have to be mounted on surfaces that have a significant amount of curvature. These include solar-powered vehicles, planes, and also some wearable devices. However, this inevitably leads to non-uniform solar irradiance among connected PV cells. If one cell among series-connected PV cells receives significantly lower solar irradiance, the overall power generation of the string is reduced. While previous works dealt with this by employing sophisticated run-time techniques, we show that design-time approaches that determine the electrical series-parallel connection of a PV array could also significantly enhance the power output. In this paper, we propose a k-means clustering-based algorithm to group PV cells/modules with similar solar irradiance to form a PV string, even allowing irregular arrays, to maximize the power generation of the array for a given irradiance profile. Our experimental results show that the power generation of a PV array could be increased by 84% compared to usual PV array organizations that do not take the curvature of the mounted surface into account. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:00 | IP3-6, 554 | PREDICTION-BASED FAST THERMOELECTRIC GENERATOR RECONFIGURATION FOR ENERGY HARVESTING FROM VEHICLE RADIATORS Speaker: Xue Lin, Northeastern University, US Authors: Hanchen Yang1, Feiyang Kang2, Caiwen Ding3, Ji Li4, Jaemin Kim5, Donkyu Baek6, Shahin Nazarian4, Xue Lin7, Paul Bogdan4 and Naehyuck Chang8 1Beijing University of Posts and Telecommunications, CN; 2Zhejiang University, CN; 3Syracuse University, US; 4University of Southern California, US; 5Seoul National University, KR; 6Korea Advanced Institute of Science and Technology, KR; 7Northeastern University, US; 8KAIST, KR Abstract Thermoelectric generation has increasingly drawn attention for being environmentally friendly. However, only a few of the prior researches on thermoelectric generators (TEG) have focused on improving efficiency at system level. They attempt to capture the electrical property changes on TEG modules as the temperature fluctuates on vehicle radiators. The most recent reconfiguration algorithm shows large improvements on output performance but suffers from major drawback on computational time and energy overhead, and non-scalability in terms of array size and processing frequency. In this paper, we propose a novel TEG array reconfiguration algorithm that determines near-optimal configuration with an acceptable computational time. More precisely, with O(N) time complexity, our prediction-based fast TEG reconfiguration algorithm enables all modules to work at or near their maximum power points (MPP). Additionally, we incorporate prediction methods to further reduce the runtime and switching overhead during the reconfiguration process. Experimental results present 30% performance improvement, almost 100x reduction on switching overhead and 13x enhancement on computational speed compared to the baseline and prior work. The scalability of our algorithm makes it applicable to larger scale systems such as industrial boilers and heat exchangers. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:01 | IP3-7, 141 | A PARAMETERIZED TIMING-AWARE FLIP-FLOP MERGING ALGORITHM FOR CLOCK POWER REDUCTION Speaker: Chaochao Feng, National University of Defense Technology, CN Authors: Chaochao Feng1, Daheng Yue1, Zhenyu Zhao1 and Zhuofan Liao2 1National University of Defense Technology, CN; 2Changsha University of Science and Technology, CN Abstract In modern integrated circuits, the clock power contributes a dominant part of the chip power. Clock power can be reduced effectively by utilizing multi-bit flip-flops. In this paper, a parameterized timing-aware flip-flop merging algorithm is proposed for clock power reduction. The single-bit flip-flops are merged into multi-bit flip-flops after placement & optimization and before clock network synthesis with consideration of function, scan chain information, distance and timing constraints. The algorithm can be configured with different parameters, such as the bit-number of MBFF, the setup timing margin and the distance margin. Experimental results under an industrial design show that compared with the basic design without MBFF, the design with 2-bit, 4-bit, 6-bit, and 8-bit MBFFs can save 7.5%, 12%, 11.8% and 11.1% total power consumption respectively. Using MBFF4 to replace 1-bit FFs is the best choice for the design optimization, which achieves minimum area and total power consumption. We also compare the designs with MBFF4 replacement under five different setup timing margins and distance margins. Without violating any timing constraint, it is better to set the setup timing margin as small as possible to achieve best power optimization. The distance margin (100μm, 30μm) is the best choice for this industry design to achieve minimum power consumption. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:02 | IP3-8, 621 | FAST CHIP-PACKAGE-PCB COANALYSIS METHODOLOGY FOR POWER INTEGRITY OF MULTI-DOMAIN HIGH-SPEED MEMORY: A CASE STUDY Speaker: Seungwon Kim, Ulsan National Institute of Science and Technology, KR Authors: Seungwon Kim1, Ki Jin Han2, Youngmin Kim3 and Seokhyeong Kang1 1Ulsan National Institute of Science and Technology (UNIST), KR; 2Dongguk University, KR; 3Kwangwoon University, KR Abstract The power integrity of high-speed interfaces is an increasingly important issue in mobile memory systems. However, because of complicated design variations such as adjacent VDD domain coupling, conventional case-specific modeling is limited in analyzing trends in results from parametric variations. Moreover, conventional industrial methods can be simulated only after the design layout is completed and it requires a lot of back-annotation processes, which result in delayed delays time to market. In this paper, we propose a chip-package-PCB coanalysis methodology applied to our multi-domain high-speed memory system model with a current generation method. Our proposed parametric simulation model can analyze the tendency of power integrity results from variable sweeps and Monte Carlo simulations, and it shows a significantly reduced runtime compared to the conventional EDA methodology under JEDEC LPPDR4 environment. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:00 | End of session Coffee Break in Exhibition Area
On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area (Terrace Level of the ICCD). Lunch Breaks (Großer Saal + Saal 1)On all conference days (Tuesday to Thursday), a seated lunch (lunch buffet) will be offered in the rooms "Großer Saal" and "Saal 1" (Saal Level of the ICCD) to fully registered conference delegates only. There will be badge control at the entrance to the lunch break area. Tuesday, March 20, 2018
Wednesday, March 21, 2018
Thursday, March 22, 2018
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