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2017-2018 Assessment of the Army Research Laboratory (2019) / Chapter Skim
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5 Computational Sciences
Pages 123-133

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From page 123...
... This chapter provides an evaluation of that work. ADVANCED COMPUTING ARCHITECTURES The advanced computing architectures group has evolved from operating and managing highperformance computing (HPC)
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From page 124...
... Challenges and Opportunities In advanced computing architectures, the Computational Sciences Campaign has identified the goals of advancing neuromorphic computing, many-core, co-processor, and ASIC-integrated architectures for data analytics and tactical HPC delivered to points of need, while meeting strict SWAPTN constraints. If a leadership role is the objective, then these goals are extremely ambitious.
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From page 125...
... The projects presented as part of advanced computing architectures need to be positioned within a larger framework so that they are motivated by the driving application or scenario to provide a complete picture. This is the key challenge for this area.
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From page 126...
... Opportunities for Computational Overmatch This work considered hardware and software opportunities and challenges, attempting to look forward 30 years and considering the consequences of Moore's law hitting a final plateau. It also considered performance-portable programming models for uncertain future hardware.
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From page 127...
... TrueNorth uses simulated leaky integrate and fire units to simulate neural computing, and is considered exciting for two reasons. First, the brain uses neurons that "fire" action potentials or "spikes." Thus, TrueNorth can be seen as coming closer to real brain computations than many other computing platforms.
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From page 128...
... The second is exploiting the potential of neuromorphic computing platforms to allow the advances in deep learning to be implemented effectively in light, low-power devices for deployment in the field. The first of these opportunities remains to be more fully exploited; this agenda lies at the heart of the data-intensive science mission and needs to be more vigorously pursued.
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From page 129...
... To achieve this, ARL needs to develop a specific collaborative process whereby university collaborators actually interact with and transfer expertise to ARL personnel. ARL has a very rich problem space to drive its research -- but to execute it ARL needs top-notch researchers and collaborators and ARL needs to invest in its own people and by promoting collaborations.
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From page 130...
... Current predictive science work at ARL focuses on developing the various "multi-" capabilities that are required for accurate computational analysis, which include multiscale, multidisciplinary, and multifidelity analysis. In each of these regimes, ARL researchers have a stated goal of including relevant verification and validation (V&V)
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From page 131...
... The integrated computational materials engineering for polycrystalline materials research effort was an excellent demonstration of the practical value of multiscale analysis as applied to the design of new materials, and it included several noteworthy goals. In particular, the project execution plan provided key data science outcomes, including a curated data repository for both experimental and computational results, and has initiated collaboration with the Georgia Tech faculty group working at the interface of material science and data science.
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From page 132...
... ARL could reform a comprehensive gap analysis of ARL predictive science efforts, toward the goal of identifying areas where collaborations are required to achieve successful R&D outcomes. This analysis could support the longer term activity of performing research outreach efforts to academia, industry, and other federal R&D institutions so that research outcomes are not compromised by omission of key components needed for successful deployment.
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From page 133...
... ARL should pursue hiring of top-notch researchers and promote collaborations with others. ARL should develop a specific collabora tive process whereby university collaborators actually interact with and transfer expertise to ARL personnel.
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Key Terms

  • neuromorphic computing
  • computational sciences
  • predictive science
  • army research
  • advanced computing

  • computing architectures
  • reinforcement learning
  • predictive sciences
  • predictive modeling
  • data analytics

  • predictive computational
  • deep learning
  • data science
  • learning methods
  • deep reinforcement

  • computing platform
  • arl researchers
  • future hardware
  • sciences campaign
  • law hitting

  • final plateau
  • uncertain future
  • programming models
  • leaky integrate
  • performing research

  • computational analysis
  • computational science
  • using deep
  • advanced armor
  • learning competition


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