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2 Analysis of the Problem
Pages 14-57

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From page 14...
... The illustration in Figure 2.1 provides structure to this discussion. At the center of the discussion and central to the diagram in Figure 2.1 is the thesis that the explosion of data and data analytics is going to both drive an increase in demand for energy but also, perhaps more importantly, reshape how that 14
From page 15...
... When considering the problem, it is imperative to first break down data and data analytics into pieces so that we can better understand how this field will impact energy consumption. Finding 1: The explosion of data creation, sharing, and use, including data analytics to support near-real-time decision making, will drive an increase in demand for energy.
From page 16...
... Thus, they needed to be collocated, which simplified the associated energy challenges even as it gave rise to large operational command centers.7 The technological disruption resulting from the commercialization of the ­Internet, the growth of cellular telephony, and the emergence of mobile computing 2  See Masters In Data Science, 2020, "What Is Data Analytics," 2U, July, https://www. mastersindatascience.org/learning/what-is-data-analytics/.
From page 17...
... These "cloud" sites require very large data centers to support both storage and computation. Because of their size and the emergence of "big data" computational approaches, the sites are energy consumption monsters, located where access to energy was assured.
From page 18...
... Finding 7: The reversal of data flows to and from the tactical edge will force the movement of computational devices to the tactical edge, which will profoundly affect the energy supply chain requirements. For the past several decades, since Desert Storm, energy consumption has been structured around large, fixed command centers directing far-flung operations.
From page 19...
... Finding 8: Movement of electricity to power data-driven operations at the tactical edge is a critical need that is more complicated than moving petroleum products. Energy Challenges in the Ecosystem The distribution of compute and store functions across the battlefield, embedded in hundreds of millions of devices, will impact energy consumption in several differ ent ways.
From page 20...
... 20  Logistics-friendly power is that which requires the least amount of variation in both form and content, so that the supply chain is not burdened with the problems of accounting and distribution of customized solutions. See NRC, 2014, Force Multiplying Technologies for Logistics Support to Military Operations, The National Academies Press, Washington, DC, https://doi.org/10.17226/18832.
From page 21...
... Typically, the analyses regarding tem perature are that of excess heat, but conflicts can take place in many different weather conditions, including very hot and very cold and in an area with many microclimates.22 Compute and storage devices may need to be cooled or warmed just to operate. Excessive heat and excessive cold can drain energy from storage devices, such as batteries, and cause power generation and transmission systems, such as diesel generators, to malfunction.23 21  See Circuitnet, 2020, "Average Temperature/Humidity for an Electronic Assembly Facility?
From page 22...
... , the commercial sector in the United States generally does not ­expect to be actively targeted for wholesale destruction by an adversary. Defense of assets and systems is a fundamental function of military operations and DoD must assume that data transmission paths and centers will be attacked, energy supply lines will be targeted, and detectable electromagnetic emissions will be exploited.
From page 23...
... The need for energy to be included in these planning efforts was acknowledged as very important. Appro­ priately including energy issues in the planning efforts includes bringing all parties 24  See NASEM, 2021, Strengthening U.S.
From page 24...
... With facilities' energy costs 28  See NASEM, 2020, Energy Challenges and Opportunities for Future Data-Driven Operations in the United States Air Force: Proceedings of a Workshop -- in Brief, The National Academies Press, Washington, DC, https://doi.org/10.17226/25872. 29  See Air Force Civil Engineer Center, 2017, "Energy Flight Plan 2017–2036," https://www.afcec.
From page 25...
... Relatively small issues, such as distributed operational energy for data systems, do not currently warrant inclu sion in the plan. With regards to the energy associated with the use of information, there is a single goal in the document: "Employ best management practices for energy efficient management of servers and data centers." There are two other explicit statements regarding data centers, one of which offers outsourcing as the preferred option: "Third-party performance contracts, such as Energy Savings Performance Contracts (ESPCs)
From page 26...
... First, aircraft fuel contributes to the generation of electricity only when the aircraft engines are activated.34 Further, advanced aircraft require significant ground support capabilities that are powered through separate genera tion systems.35 Last, the proliferation of information technology within and sur rounding operations all require electricity to operate.36 The inexorable conclusion is that energy needs at the tactical edge separate from aircraft fuel are an increas ingly important issue. The focus of this report is on the energy needs for the data processing at the tactical edge.
From page 27...
... Will Network Sensors, Wearables, Weapons, Munitions, and Platforms for Information Dominance," International Defense, Security and Technology, August 8, https://idstch.com/cyber/internet-things-battlefield/. 40  See Manufacturing.net, 2013, "7 Ways Signal Noise Can Impact Your Electrical Equipment," November 13, https://www.manufacturing.net/industry40/article/13057416/7-ways-signal-noise can-impact-your-electrical-equipment.
From page 28...
... Finding 14: There will be energy needs associated with the proliferation of smart devices, AI applications, and unmanned systems. Batteries serve as extremely useful forms of portable energy, especially for small or distributed devices.
From page 29...
... Military installations should be engaged with commercial energy providers to ensure that planning includes known expansion or increases in energy demands for their tenant organizations.46 Except in wartime conditions, military installation expansion intentions are well known years in advance, which should allow for sufficient planning time with commercial energy providers. The units that deploy to the tactical edge originate at these installations and can be assumed to deploy with doctrinal energy support infrastructures, as well as with an initial supply of energy.
From page 30...
... Finding 16: The initial deployment of a unit to the tactical edge will include energy that originates from the fixed installations, including energy from com mercial energy provider partners. Support for Machine Learning and Artificial Intelligence Machine learning (ML)
From page 31...
... Bailey, 2019, "Power Is Limiting Machine Learning Deployments," SemiEngineering, July 25, https://semiengineering.com/ power-limitations-of-machine-learning/.
From page 32...
... Most of the Time," New York Times, June 6, https://www.nytimes.com/2019/06/06/smarter-living/wirecutter/are-rechargeable batteries-better-than-alkaline.html. 55  See NASEM, 2020, Energy Challenges and Opportunities for Future Data-Driven Operations in the United States Air Force: Proceedings of a Workshop -- in Brief, The National Academies Press, Washington, DC, https://doi.org/10.17226/25872.
From page 33...
... Without data, systems cannot see, hear, or speak. Compounding these issues, it is currently not a requirement that data depen dencies or needs be included in weapon systems specifications.57 Because data dependencies and needs are not specified, it follows that the energy required for both direct data dependencies and indirect data dependencies for weapon systems readiness and operational capabilities, particularly when deployed, may not be well understood.
From page 34...
... There is an increasing trend toward edge computing and disaggregation, which is a data architecture with different requirements from a model of cloud-based or otherwise centralized computing centers.58 In parallel with the trend toward edge computing, however, there is also a trend toward optimizing energy efficiency of computing hardware by specializing to computing chip platforms tailored to neural network machine learning (such as Google's tensor processing units [TPUs] 59 and 58  See NASEM, 2020, Energy Challenges and Opportunities for Future Data-Driven Operations in the United States Air Force: Proceedings of a Workshop -- in Brief, The National Academies Press, Washington, DC, https://doi.org/10.17226/25872, presentations by Mukhopadhyay, M
From page 35...
... • How, in what time scales, and under which preprocessing, compression, and data fusion models do the data required for training and inference in ML algorithms flow across a disaggregated network? • When is a data architecture constrained by energy costs versus bandwidth limitations to move the data to where they need to be processed?
From page 36...
... Finding 23: There is a trend toward optimizing energy efficiency of comput ing hardware by specializing to computing chip platforms tailored to neural network machine learning. Finding 24: Mission-specific applications require further processor development to realize similar gains in reducing processing time and energy requirements.
From page 37...
... The process of dynamic speed scaling in variable speed processors can save energy by utilizing the full speed and frequency capabili ties of a processor and utilizing low speeds whenever possible.66 Experiments to understand energy consumption for algorithms have been performed67 for popular vector operations, matrix operations, sorting, and graph algorithms. Energy con sumption for a given algorithm depends on the extent to which that algorithm can exhibit memory parallelism for a given data layout in the random access memory (RAM)
From page 38...
... .72 Many data-rich applications in machine learning, signal processing, com puter vision, and cognitive computing have inherent error resiliency: noisy inputs, ­approximate algorithms, and loose constraint on output. Approximate computa tion can be leveraged to trade off accuracy for hardware resources (e.g., design area, power, latency)
From page 39...
... Finding 26: Clever algorithm design can yield orders of magnitude of energy savings for computer energy consumption. Further, this body of knowledge suggests pathways in research that can expand fundamental ideas surrounding energy-aware numerical algorithms and research on practical and prototyped algorithms for energy-aware computations.
From page 40...
... Hardware specialization within heterogeneous computing systems with custom processing circuits results in the elimination of many loads and stores, in addition to executing all possible concurrent operations simultaneously. In terms of hardware for data intensive processing, heterogeneous computing systems provide the best energy efficiency for data rich applications.78 Box 2.1 discusses the current state of machine learning hardware in more detail.
From page 41...
... 1See F Konstantinidis, 2020, "Why and How to Run Machine Learning Algorithms on Edge Devices," The Robot Report, February 23.
From page 42...
... There is an entire research field associated with iden tifying energy-efficient computational approaches, which encompass both hard ware and software aspects. To date, "researchers have proved mathematically that relatively simple hardware modifications could cut in half the energy consumed in running today's standard software procedures."81 The implications of efficient computing cascade to the energy needed for cooling as well: fewer computational cycles generate less heat.
From page 43...
... As noted in the section "Energy Consumption Reduction Architectures" above, the choices of applications and engineering solutions that depend on big data can greatly affect energy requirements for tactical-edge computing support. The next-generation tactical-edge information collection, processing, and usage is con ceptually based on what can be referred to as "big data," which is intended to feed machine learning support to operations.
From page 44...
... Because of the huge energy consumption needs, the details of implementation are important: ­energy losses over transmission lines can figure quite significantly in the require ments calculations. That is why very large data centers are located as close as pos sible to power generation facilities.88 85  See Big Data, 2020, "The Five V's of Big Data," BBVA, May 26, https://www.bbva.com/en/five vs-big-data/.
From page 45...
... Finding 32: Dependence on conventional cloud computing solutions is not a viable solution for every tactical situation and therefore cannot be the sole solution to the data-driven operational problems. The development of portable data centers may provide an intermediate solution.
From page 46...
... architecture model for training AI systems is to aggregate training data in a large, centralized computing center, followed by deployment of the trained model to the edge. Such a data architecture may be appropriate when 89  See NASEM, 2020, Energy Challenges and Opportunities for Future Data-Driven Operations in the United States Air Force: Proceedings of a Workshop -- in Brief, The National Academies Press, Washington, DC, https://doi.org/10.17226/25872, presentation by Col.
From page 47...
... Data fusion as an aspect of data architecture design is worth calling out. When mission applications may be enhanced or even reliant on edge node data fusion capabilities, data processing and communications requirements must be set at the system or architecture level.
From page 48...
... Lapedus, 2019, "Engineering Talent Shortage Now Top Risk Factor," Semiengineering, February 25, https://semiengineering.com/engineering-talent-shortage now-top-risk-factor/. 94  See Air Force Civil Engineer Center, 2017, "Energy Flight Plan 2017–2036," https://www.afcec.
From page 49...
... For the purpose of this discussion, the term "manpower" includes recruiting, educating, training, contractor/military blend, and incentives for education. Finding 40: The USAF does not have the organic manpower to manage, lead, supervise, or solve the challenges associated with the design, operation, and management of energy consumption associated with data-driven operations.
From page 50...
... ." See North American Electric Reliability Corporation, 2017, "Distributed Energy Resources," February, https:// www.nerc.com/comm/other/essntlrlbltysrvcstskfrcdl/distributed_energy_resources_report.pdf. 99  See National Renewable Energy Laboratory, 2021, "Renewable Electricity Futures Study," https:// www.nrel.gov/analysis/re-futures.html.
From page 51...
... 101  See Office of Energy Efficiency and Renewable Energy, 2020, "2020 Assisting Federal Facilities with Energy Conservation Technologies (AFFECT) Federal Agency Call Funding Recipients," https:// www.energy.gov/eere/femp/2020-assisting-federal-facilities-energy-conservation-technologies affect-federal-agency.
From page 52...
... The reliance on traditional energy sources, such as diesel generators, can imbue vulnerabilities into the system that can be ameliorated with alternative energy sourcing and ­storage. Having multiple energy sources with versatile storage capabilities can be an advantage.107 There are several ways to think about increasing the resiliency of energy systems in order to minimize effects from direct or indirect attacks, including those result ing from natural phenomena such as weather.
From page 53...
... Therefore, understanding the types and potential of threats to energy sources throughout the supply chain is important. The Threat Ecosystem Threats come in many flavors, including those yet to be imagined.
From page 54...
... The management of energy use at the tactical edge includes the systems for converting stored energy to usable energy, the infrastruc ture through which energy flows from source to user, the administration of scarce resources, and the communications of energy requirements for both future and current operations. Transforming a source of energy into a usable form of energy is an important first step in the supply chain.
From page 55...
... Another attack vector -- a compromise of the technology supply system -- was used in con junction with the SolarWinds incident in late 2020.114 The technology supply chain is different from the energy supply chain discussed above. Rather than disrupting 111  See Army Technology, 2020, "Casualty Costs of Fuel and Water Resupply Convoys in Afghanistan and Iraq," February 7, https://www.army-technology.com/features/feature77200/.
From page 56...
... Renewable energy (wind, solar, etc.) may not provide the steady base load of com mercial power needed by military installations.
From page 57...
... The electric grid functions at three main layers -- generation, transmission, and distribution. Disruptions in any of these layers can cause loss of power to military installations and other customers.


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