Date of Award
May 2014
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering and Computer Science
Advisor(s)
Qinru Qiu
Keywords
Brain State in a Box, High Performance Computing, Neuromorphic Computing, Optimization, Xeon Phi
Subject Categories
Engineering
Abstract
Neuromorphic computing systems refer to the computing architecture inspired by the working mechanism of human brains. The rapidly reducing cost and increasing performance of state-of-the-art computing hardware allows large-scale implementation of machine intelligence models with neuromorphic architectures and opens the opportunity for new applications. One such computing hardware is Intel Xeon Phi coprocessor, which delivers over a TeraFLOP of computing power with 61 integrated processing cores. How to efficiently harness such computing power to achieve real time decision and cognition is one of the key design considerations. This work presents an optimized implementation of Brain-State-in-a-Box (BSB) neural network model on the Xeon Phi coprocessor for pattern matching in the context of intelligent text recognition of noisy document images. From a scalability standpoint on a High Performance Computing (HPC) platform we show that efficient workload partitioning and resource management can double the performance of this many-core architecture for neuromorphic applications.
Access
Open Access
Recommended Citation
Ahmed, Khadeer, "Accelerating Pattern Matching in Neuromorphic Text Recognition System Using Intel Xeon Phi Coprocessor" (2014). Theses - ALL. 37.
https://surface.syr.edu/thesis/37
