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1. Dynamic Metasurface Antennas for Energy-Efficient MISO Communications

   https://doi.org/10.1109/GLOBECOM54140.2023.10436779  

   Carlson, Joseph ; Castellanos, Miguel R. ; Heath, Robert W. ( December 2023 , IEEE)

   Dynamic metasurface antennas (DMA) have been proposed for massive multiple-input multiple-output (MIMO) and millimeter wave applications due to their ability to cre- ate dense, energy-efficient arrays. In this paper, we integrate DMAs into a realistic wireless environment to compare their performance in spectral and energy efficiency with a conventional phased array. We implement a practical transmitter architecture for the DMA and phased array to account for the power consumption and hardware constraints of the radio frequency (RF) front end. Simulation results for a MISO scenario show that while the DMA performs worse in spectral efficiency than an active phased array, the power consumption savings from the reconfigurable component enable better performance in energy efficiency. Therefore, DMAs can provide an energy-efficient alternative to typical phased arrays. 

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2. Optimal Policy for Deployment of Machine Learning Models on Energy-Bounded Systems

   https://doi.org/10.24963/ijcai.2020/473  

   Mirzadeh, Seyed Iman ; Ghasemzadeh, Hassan ( July 2020 , Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence)

   null (Ed.)

   With the recent advances in both machine learning and embedded systems research, the demand to deploy computational models for real-time execution on edge devices has increased substantially. Without deploying computational models on edge devices, the frequent transmission of sensor data to the cloud results in rapid battery draining due to the energy consumption of wireless data transmission. This rapid power dissipation leads to a considerable reduction in the battery lifetime of the system, therefore jeopardizing the real-world utility of smart devices. It is well-established that for difficult machine learning tasks, models with higher performance often require more computation power and thus are not power-efficient choices for deployment on edge devices. However, the trade-offs between performance and power consumption are not well studied. While numerous methods (e.g., model compression) have been developed to obtain an optimal model, these methods focus on improving the efficiency of a single model. In an entirely new direction, we introduce an effective method to find a combination of multiple models that are optimal in terms of power-efficiency and performance by solving an optimization problem in which both performance and power consumption are taken into account. Experimental results demonstrate that on the ImageNet dataset, we can achieve a 20% energy reduction with only 0.3% accuracy drop compared to Squeeze-and-Excitation Networks. Compared to a pruned convolutional neural network for human activity recognition, while consuming 1.7% less energy, our proposed policy achieves 1.3% higher accuracy. 

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3. NEBULA: A Neuromorphic Spin-Based Ultra-Low Power Architecture for SNNs and ANNs

   https://doi.org/10.1109/ISCA45697.2020.00039  

   Singh, Sonali ; Sarma, Anup ; Jao, Nicholas ; Pattnaik, Ashutosh ; Lu, Sen ; Yang, Kezhou ; Sengupta, Abhronil ; Narayanan, Vijaykrishnan ; Das, Chita R. ( May 2020 , 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA))

   null (Ed.)

   Brain-inspired cognitive computing has so far followed two major approaches - one uses multi-layered artificial neural networks (ANNs) to perform pattern-recognition-related tasks, whereas the other uses spiking neural networks (SNNs) to emulate biological neurons in an attempt to be as efficient and fault-tolerant as the brain. While there has been considerable progress in the former area due to a combination of effective training algorithms and acceleration platforms, the latter is still in its infancy due to the lack of both. SNNs have a distinct advantage over their ANN counterparts in that they are capable of operating in an event-driven manner, thus consuming very low power. Several recent efforts have proposed various SNN hardware design alternatives, however, these designs still incur considerable energy overheads.In this context, this paper proposes a comprehensive design spanning across the device, circuit, architecture and algorithm levels to build an ultra low-power architecture for SNN and ANN inference. For this, we use spintronics-based magnetic tunnel junction (MTJ) devices that have been shown to function as both neuro-synaptic crossbars as well as thresholding neurons and can operate at ultra low voltage and current levels. Using this MTJ-based neuron model and synaptic connections, we design a low power chip that has the flexibility to be deployed for inference of SNNs, ANNs as well as a combination of SNN-ANN hybrid networks - a distinct advantage compared to prior works. We demonstrate the competitive performance and energy efficiency of the SNNs as well as hybrid models on a suite of workloads. Our evaluations show that the proposed design, NEBULA, is up to 7.9× more energy efficient than a state-of-the-art design, ISAAC, in the ANN mode. In the SNN mode, our design is about 45× more energy-efficient than a contemporary SNN architecture, INXS. Power comparison between NEBULA ANN and SNN modes indicates that the latter is at least 6.25× more power-efficient for the observed benchmarks. 

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4. Accurately Simulating Energy Consumption of I/O-intensive Scientific Workflows

   https://doi.org/10.1007/978-3-030-22734-0_11  

   Ferreira da Silva, Rafael ; Orgerie, Anne-Cecile ; Casanova, Henri ; Tanaka, Ryan ; Deelman, Ewa ; Suter, Frederic ( July 2019 , International Conference on Computational Science (ICCS))

   While distributed computing infrastructures can provide infrastructure-level techniques for managing energy consumption, application-level energy consumption models have also been developed to support energy-efficient scheduling and resource provisioning algorithms. In this work, we analyze the accuracy of a widely-used application-level model that have been developed and used in the context of scientific workflow executions. To this end, we profile two production scientific workflows on a distributed platform instrumented with power meters. We then conduct an analysis of power and energy consumption measurements. This analysis shows that power consumption is not linearly related to CPU utilization and that I/O operations significantly impact power, and thus energy, consumption. We then propose a power consumption model that accounts for I/O operations, including the impact of waiting for these operations to complete, and for concurrent task executions on multi-socket, multi-core compute nodes. We implement our proposed model as part of a simulator that allows us to draw direct comparisons between real-world and modeled power and energy consumption. We find that our model has high accuracy when compared to real-world executions. Furthermore, our model improves accuracy by about two orders of magnitude when compared to the traditional models used in the energy-efficient workflow scheduling literature. 

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5. A Power Efficiency Metric for Comparing Energy Consumption in Future Wireless Networks in the Millimeter Wave and Terahertz bands

   https://doi.org/10.1109/MWC.005.2200083  

   Kanhere, Ojas ; Poddar, Hitesh ; Xing, Yunchou ; Shakya, Dipankar ; Ju, Shihao ; Rappaport, Theodore S. ( December 2022 , IEEE Wireless Communications)

   Future wireless cellular networks will utilize millimeter- wave and sub-THz frequencies and deploy small-cell base stations to achieve data rates on the order of hundreds of gigabits per second per user. The move to sub-THz frequencies will require attention to sustainability and reduction of power whenever possible to reduce the carbon footprint while maintaining adequate battery life for the massive number of resource-constrained devices to be deployed. This article analyzes power consumption of future wireless networks using a new metric, a figure of merit called the power waste factor (W), which shows promise for the study and development of “green G” — green technology for future wireless networks. Using W, power efficiency can be considered by quantifying the power wasted by all devices on a signal path in a cascade. We then show that the consumption efficiency factor (CEF), defined as the ratio of the maximum data rate achieved to the total power consumed, is a novel and powerful measure of power efficiency which shows that less energy per bit is expended as the cell size shrinks and carrier frequency and channel bandwidth increase. Our findings offer a standard approach to calculating and comparing power consumption and energy efficiency. 

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