Deep Learning and Inference Using Models with Low Precision Synapses and Binary Unit Activations
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Research Topic Description, including Problem Statement:
Deep learning has revolutionized the field of machine learning. However, current approaches to deep neural network training require backpropagation of errors with high precision. This poses a challenge for training deep neural networks on future generation, low-power edge computing platforms under the constraint of low-precision (possibly binary) weights and binary unit activations. Recent research on binary neural networks (BNNs) and spiking neural networks (SNNs) offers hope that a solution to this problem can be found. However, this remains an open problem. Approaches may involve simulators, field-programmable gate arrays (FPGAs), and/or neuromorphic hardware.
Example Approaches:
- “EventProp: Backpropagation for Exact Gradients in Spiking Neural Networks” arXiv:2009.08378
- “Training Binary Neural Networks with Real-to-Binary Convolutions” arXiv:2003.11535
Relevance to the Intelligence Community:
The Intelligence Community (IC) will increasingly rely on edge computing platforms to detect patterns of interest in sensor data. Developing algorithms for training and inference on future generation, low-power edge computing platforms will ensure that the IC is able to take full advantage of these platforms.
Key Words: Deep Learning, Machine Learning, Artificial Intelligence, Neuromorphic, Low Precision, Event Based Computing, Neural Networks, SNN, BNN
Postdoc Eligibility
- U.S. citizens only
- Ph.D. in a relevant field must be completed before beginning the appointment and within five years of the application deadline
- Proposal must be associated with an accredited U.S. university, college, or U.S. government laboratory
- Eligible candidates may only receive one award from the IC Postdoctoral Research Fellowship Program
Research Advisor Eligibility
- Must be an employee of an accredited U.S. university, college or U.S. government laboratory
- Are not required to be U.S. citizens
- Citizenship: U.S. Citizen Only
- Degree: Doctoral Degree.
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Discipline(s):
- Chemistry and Materials Sciences (12 )
- Communications and Graphics Design (2 )
- Computer, Information, and Data Sciences (17 )
- Earth and Geosciences (21 )
- Engineering (27 )
- Environmental and Marine Sciences (14 )
- Life Health and Medical Sciences (45 )
- Mathematics and Statistics (10 )
- Other Non-Science & Engineering (2 )
- Physics (16 )
- Science & Engineering-related (1 )
- Social and Behavioral Sciences (27 )
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