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5 Computational Sciences
Pages 78-88

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From page 78...
... Accomplishments and Advancements The group has made significant progress in evaluating the role of neuromorphic computing using IBM's TrueNorth processor and its leaky "integrate and fire" framework to enable high-fidelity computation using many low-precision elements, and thus very low energy. TrueNorth uses simulated leaky integrate and fire units to simulate neural computing.
From page 79...
... Moreover, the project's methodology of evaluation through simulation is a nice example of leveraging the HPC expertise at ARL. Opportunities and Challenges 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.
From page 80...
... They are "internal tooling" projects, well worth the doing, that will enable easier programming and improved portability for future software projects. 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.
From page 81...
... DATA-INTENSIVE SCIENCES The data-intensive sciences program presented its goals and progress in applied machine learning (ML) , neuromorphic computing, and cooperative multiagent control using deep reinforcement learning.
From page 82...
... The laboratory's efforts in neuromorphic computing have blossomed since the panel's last visit into a significant and highly collaborative research effort. In the Army High-Performance Computing Research Center, exciting progress has also been made using deep reinforcement learning methods for multiagent control.
From page 83...
... Relying on the leaky integrate and fire model of the neuron is one important approach, but far from the only one, and it is not clear that it is ultimately advantageous from a computational point of view to make use of spiking neurons. Today's deep learning methods rely on the use of continuous functions to allow very deep continuous computational paths to be subject to gradient descent -- their power depends on their differentiability, whereas a "spiking" event is discrete and not differentiable.
From page 84...
... ARL has a very rich and challenging set of real-world problems that provide exciting challenges to machine learning researchers within ARL and elsewhere. To achieve this, ARL needs to develop a specific collaborative process whereby university collaborators actually interact with and transfer expertise to ARL personnel.
From page 85...
... The first challenge is that the cultural change required for broad acceptance of predictive modeling and simulation may not yet be in place, and it is not yet clear whether there has been a widespread adoption of the V&V and UQ technologies required for computational analyses to be demonstrated as truly "predictive." Current ARL computational analysis practice does not yet promote standardized UQ processes, and V&V activities were notable not by demonstration, but by more commonly being omitted in discussions of R&D project activities. This lack of acceptance of emerging methods for demonstrating the predictive effectiveness of computational simulation was specifically called out in the 2015 ARLTAB review.
From page 86...
... Kudos for trying to look forward a good 30 years and thinking about the consequences of Moore's law hitting a final plateau, and also for thinking about performance-portable programming models for uncertain future hardware. The data-intensive sciences overview and the two presentations on neuromorphic processing and cooperative reinforcement learning were excellent.
From page 87...
... Also, ARL should maintain an effort to track out-of-the-box concepts that could prove to be of interest to ARL. In particular, ARL should focus on those that would have crosscutting benefits to more than one of the three areas in the Computational Sciences Campaign.
From page 88...
... researchers and promote collaborations with others. ARL should develop a specific collaborative process whereby university collaborators actually interact with and transfer expertise to ARL personnel.


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