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Accession Number:
AD1079216
Title:
Reservoir Computing and Benchmarking of Neuromorphic Systems for Swap-Constrained Autonomous Processing
Descriptive Note:
[Technical Report, Final Report]
Corporate Author:
Rochester Institute of Technology
Report Date:
2019-08-26
Pagination or Media Count:
92
Abstract:
Random projection networks are a class of learning algorithm, which use high-dimensional random projections of information as a basis for training simple linear classifiers. The advantage of this approach is the lite computational cost associated with learning and short training times. These advantages make random projection networks suitable candidates for implementing on-device learning systems for on-the-edge intelligence. In particular, we focus on a feed forward RPN known as the Extreme Learning Machine ELM as a baseline for spatial information, and two recurrent RPNs, known as the Liquid State Machine LSM and Echo State Network ESN, for processing spatio-temporal information. The fist focus of this work was to investigate advancements at an algorithmic level to make the algorithms more computationally powerful and hardware friendly. Secondly, we developed several baseline architectures for stochastic, digital, and analog implementations for size, weight, and power SWaP constrained platforms with on-device learning.
Distribution Statement:
[A, Approved For Public Release]
