Assessing System-Level Energy Efficiency of mmWave-Based Wearable Networks

2016 ◽  
Vol 34 (4) ◽  
pp. 923-937 ◽  
Author(s):  
Olga Galinina ◽  
Alexander Pyattaev ◽  
Kerstin Johnsson ◽  
Andrey Turlikov ◽  
Sergey Andreev ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
System Level ◽  
Level Energy

scholarly journals An Integrated Circuit for Radio Astronomy Correlators Supporting Large Arrays of Antennas

2016 ◽  
Vol 05 (02) ◽  
pp. 1650002 ◽  
Author(s):  
Larry R. D’Addario ◽  
Douglas Wang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Integrated Circuit ◽  
Cross Correlation ◽  
Silicon On Insulator ◽  
System Level ◽  
Level Energy ◽  
Silicon Area ◽  
Power Efficient ◽  
The Cost

Radio telescopes that employ arrays of many antennas are in operation, and ever larger ones are being designed and proposed. Signals from the antennas are combined by cross-correlation. While the cost of most components of the telescope is proportional to the number of antennas N, the cost and power consumption of cross-correlation are proportional to [Formula: see text] and dominate at sufficiently large N. Here, we report the design of an integrated circuit (IC) that performs digital cross-correlations for arbitrarily many antennas in a power-efficient way. It uses an intrinsically low-power architecture in which the movement of data between devices is minimized. In a large system, each IC performs correlations for all pairs of antennas but for a portion of the telescope’s bandwidth (the so-called “FX” structure). In our design, the correlations are performed in an array of 4096 complex multiply-accumulate (CMAC) units. This is sufficient to perform all correlations in parallel for 64 signals (N[Formula: see text]=[Formula: see text]32 antennas with two opposite-polarization signals per antenna). When N is larger, the input data are buffered in an on-chip memory and the CMACs are reused as many times as needed to compute all correlations. The design has been synthesized and simulated so as to obtain accurate estimates of the ICs size and power consumption. It is intended for fabrication in a 32[Formula: see text]nm silicon-on-insulator process, where it will require less than 12[Formula: see text]mm2 of silicon area and achieve an energy efficiency of 1.76–3.3[Formula: see text]pJ per CMAC operation, depending on the number of antennas. Operation has been analyzed in detail up to [Formula: see text]. The system-level energy efficiency, including board-level I/O, power supplies, and controls, is expected to be 5–7[Formula: see text]pJ per CMAC operation. Existing correlators for the JVLA ([Formula: see text]) and ALMA ([Formula: see text]) telescopes achieve about 5000[Formula: see text]pJ and 1000[Formula: see text]pJ, respectively using application-specific ICs (ASICs) in older technologies. To our knowledge, the largest-N existing correlator is LEDA at [Formula: see text]; it uses GPUs built in 28[Formula: see text]nm technology and achieves about 1000[Formula: see text]pJ. Correlators being designed for the SKA telescopes ([Formula: see text] and [Formula: see text]) using FPGAs in 16[Formula: see text]nm technology are predicted to achieve about 100[Formula: see text]pJ.

scholarly journals A Comparative Study of Methods for Measurement of Energy of Computing

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2204 ◽  
Author(s):  
Muhammad Fahad ◽  
Arsalan Shahid ◽  
Ravi Reddy Manumachu ◽  
Alexey Lastovetsky
Keyword(s):  
Predictive Models ◽  
Ground Truth ◽  
System Level ◽  
Average Error ◽  
Level Energy ◽  
Physical Measurements ◽  
Wide Range ◽  
External Power ◽  
On Chip ◽  
Multicore Cpus

Energy of computing is a serious environmental concern and mitigating it is an important technological challenge. Accurate measurement of energy consumption during an application execution is key to application-level energy minimization techniques. There are three popular approaches to providing it: (a) System-level physical measurements using external power meters; (b) Measurements using on-chip power sensors and (c) Energy predictive models. In this work, we present a comprehensive study comparing the accuracy of state-of-the-art on-chip power sensors and energy predictive models against system-level physical measurements using external power meters, which we consider to be the ground truth. We show that the average error of the dynamic energy profiles obtained using on-chip power sensors can be as high as 73% and the maximum reaches 300% for two scientific applications, matrix-matrix multiplication and 2D fast Fourier transform for a wide range of problem sizes. The applications are executed on three modern Intel multicore CPUs, two Nvidia GPUs and an Intel Xeon Phi accelerator. The average error of the energy predictive models employing performance monitoring counters (PMCs) as predictor variables can be as high as 32% and the maximum reaches 100% for a diverse set of seventeen benchmarks executed on two Intel multicore CPUs (one Haswell and the other Skylake). We also demonstrate that using inaccurate energy measurements provided by on-chip sensors for dynamic energy optimization can result in significant energy losses up to 84%. We show that, owing to the nature of the deviations of the energy measurements provided by on-chip sensors from the ground truth, calibration can not improve the accuracy of the on-chip sensors to an extent that can allow them to be used in optimization of applications for dynamic energy. Finally, we present the lessons learned, our recommendations for the use of on-chip sensors and energy predictive models and future directions.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

2020 ◽  
Vol 2674 (6) ◽  
pp. 253-267 ◽  
Author(s):  
Iman Mahdinia ◽  
Ramin Arvin ◽  
Asad J. Khattak ◽  
Amir Ghiasi
Keyword(s):  
Fuel Consumption ◽  
Adaptive Cruise Control ◽  
Field Tests ◽  
System Level ◽  
Level Energy ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Cooperative Adaptive Cruise Control ◽  
Vehicle Platoon ◽  
Fuel Consumption And Emissions

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

scholarly journals Editorial for the Special Issue on Emerging Memory and Computing Devices in the Era of Intelligent Machines

Micromachines ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 73
Author(s):  
Pedram Khalili Amiri
Keyword(s):  
Energy Efficiency ◽  
System Level ◽  
Special Issue ◽  
Computing Systems ◽  
Intelligent Machines ◽  
Emerging Memory ◽  
Overall Performance

Computing systems are undergoing a transformation from logic-centric toward memory-centric architectures, where overall performance and energy efficiency at the system level are determined by the density, bandwidth, latency, and energy efficiency of the memory, rather than the logic sub-system [...]

Sign in / Sign up

Export Citation Format

Share Document