Current and future climate impacts of aviation emissions are quantified using a combination of atmospheric models, surface and satellite observations, and laboratory experiments. IMPACT OF AVIATION ON CLIMATEFAA's Aviation Climate Change Research Initiative (ACCRI) Phase II by Guy P. brasseur, Mohan GuPta, bruce e. anderson, sathya balasubraManian, steven barrett, david duda, GreGG FleMinG, Piers M. Forster, Jan FuGlestvedt, andrew GettelMan, ranGasayi n. halthore, s. daniel Jacob, Mark Z. Jacobson, areZoo khodayari, kuo-nan liou, Marianne t. lund, richard c. Miake-lye, Patrick Minnis, seth olsen, Joyce e. Penner, ronald Prinn, ulrich schuMann, henry b. selkirk, andrei sokolov, nadine unGer, PhiliP wolFe, hsi-wu wonG, donald w. wuebbles, binGqi yi, PinG yanG, and chenG Zhou D uring the course of flight, aircraft burn fuel and emit gases and particles into the atmosphere, primarily at cruise altitudes within the upper troposphere and the lower stratosphere (UTLS).These emissions include carbon dioxide (CO 2 ), water vapor (H 2 O), hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO x or NO + NO 2 ), sulfur oxides (SO x ), and nonvolatile black carbon (BC or AFFILIATIONS: brasseur-Max Planck Institute for Meteorology, Hamburg, Germany, and National Center for Atmospheric Research, Boulder, Colorado; GuPta, halthore, and Jacob-Federal Aviation Administration, Washington, d.c.; anderson and Minnis-nasa Langley Research Center, Hampton, Virginia; balasubraManian and FleMinG-Volpe Center, Department of Transportation, Cambridge, Massachusetts; barrett, Prinn, sokolov, and wolFe-Massachusetts Institute of Technology, Cambridge, Massachusetts; dudassai/nasa Langley Research Center, Hampton, Virginia; ForsterUniversity of Leeds, Leeds, United Kingdom; FuGlestvedt and lundcicero, Norway; GettelMan-National Center for Atmospheric Research, Boulder, Colorado; Jacobson-Stanford University, Palo Alto, California; khodayari*, olsen, and wuebbles-University of Illinois at Urbana-Champaign, Champaign, Illinois; liou-University of California, Los Angeles, Los Angeles, California; Miake-lye and wonG*-Aerodyne Research Inc., Billerica, Massachusetts; Penner and Zhou-University of Michigan, Ann Arbor, Michigan; The impact of these emissions on UTLS has been examined for several decades (Schumann 1994;Brasseur et al. 1998;Penner et al. 1999;Lee et al. 2009 1 Gaseous emissions of SO x and NO x evolve and partially transform into volatile nitrate and sulfate aerosols and those of gaseous HC emissions into semivolatile organic particles, which also contribute to climate change. Particles like sulfates generally have a cooling effect (negative RF) unless they coat soot particles, which exert warming effects. Note that BC particles are normally considered to be the main component of soot particles.Persistent linear contrails produced in the wake of aircraft contribute to net climate warming. Contrailinduced cirrus clouds (AIC) are also expected to affect the solar and terrestrial infrared radiative budget of the atmosphere, but t...

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Generative Adversarial Networks (GANs) are one of the most recent deep learning models that generate synthetic data from limited genuine datasets. GANs are on the frontier as further extension of deep learning into many domains (e.g., medicine, robotics, content synthesis) requires massive sets of labeled data that is generally either unavailable or prohibitively costly to collect. Although GANs are gaining prominence in various fields, there are no accelerators for these new models. In fact, GANs leverage a new operator, called transposed convolution, that exposes unique challenges for hardware acceleration. This operator first inserts zeros within the multidimensional input, then convolves a kernel over this expanded array to add information to the embedded zeros. Even though there is a convolution stage in this operator, the inserted zeros lead to underutilization of the compute resources when a conventional convolution accelerator is employed. We propose the GANAX architecture to alleviate the sources of inefficiency associated with the acceleration of GANs using conventional convolution accelerators, making the first GAN accelerator design possible. We propose a reorganization of the output computations to allocate compute rows with similar patterns of zeros to adjacent processing engines, which also avoids inconsequential multiply-adds on the zeros. This compulsory adjacency reclaims data reuse across these neighboring processing engines, which had otherwise diminished due to the inserted zeros. The reordering breaks the full SIMD execution model, which is prominent in convolution accelerators. Therefore, we propose a unified MIMD-SIMD design for GANAX that leverages repeated patterns in the computation to create distinct microprograms that execute concurrently in SIMD mode. The interleaving of MIMD and SIMD modes is performed at the granularity of single microprogrammed operation. To amortize the cost of MIMD execution, we propose a decoupling of data access from data processing in GANAX. This decoupling leads to a new design that breaks each processing engine to an access micro-engine and an execute micro-engine. The proposed architecture extends the concept of access-execute architectures to the finest granularity of computation for each individual operand. Evaluations with six GAN models shows, on average, 3.6× speedup and 3.1× energy savings over EYERISS without compromising the efficiency of conventional convolution accelerators. These benefits come with a mere ≈7.8% area increase. These results suggest that GANAX is an effective initial step that paves the way for accelerating the next generation of deep neural models.

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