Analyzing the Scaling of Connectivity in Neuromorphic Hardware and in Models of Neural Networks

In recent years, neuromorphic hardware systems have significantly grown in size. With more and more neurons and synapses integrated in such systems, the neural connectivity and its configurability have become crucial design constraints. To tackle this problem, we introduce a generic extended graph d...

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Veröffentlicht in:IEEE transactions on neural networks Jg. 22; H. 6; S. 919 - 935
Hauptverfasser: Partzsch, J, Schuffny, R
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.06.2011
Institute of Electrical and Electronics Engineers
Schlagworte:
Algorithms
Analytical models
Applied sciences
Artificial intelligence
Artificial neural networks
Biological system modeling
Computational modeling
Computer science; control theory; systems
Computer Simulation
Computer systems and distributed systems. User interface
Computer-Aided Design
Connectionism. Neural networks
Connectivity
Equipment Design
Equipment Failure Analysis
Exact sciences and technology
Hardware
Image Interpretation, Computer-Assisted - methods
Models, Theoretical
network scaling
network topology
Neural Networks (Computer)
neuromorphic hardware
Neuromorphics
Neurons
Pattern Recognition, Automated - methods
Signal Processing, Computer-Assisted - instrumentation
Software
Circuit design
Locality
neuromorphic hardware
Neural network
Distributed system
network scaling
Computational complexity
Scale model
Modeling
Circuit architecture
Synapse
Network topology
Biomimetics
Classification
Size effect
Connectivity
ISSN:1045-9227, 1941-0093, 1941-0093
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Zusammenfassung:In recent years, neuromorphic hardware systems have significantly grown in size. With more and more neurons and synapses integrated in such systems, the neural connectivity and its configurability have become crucial design constraints. To tackle this problem, we introduce a generic extended graph description of connection topologies that allows a systematical analysis of connectivity in both neuromorphic hardware and neural network models. The unifying nature of our approach enables a close exchange between hardware and models. For an existing hardware system, the optimally matched network model can be extracted. Inversely, a hardware architecture may be fitted to a particular model network topology with our description method. As a further strength, the extended graph can be used to quantify the amount of configurability for a certain network topology. This is a hardware design variable that has widely been neglected, mainly because of a missing analysis method. To condense our analysis results, we develop a classification for the scaling complexity of network models and neuromorphic hardware, based on the total number of connections and the configurability. We find a gap between several models and existing hardware, making these hardware systems either impossible or inefficient to use for scaled-up network models. In this respect, our analysis results suggest models with locality in their connections as promising approach for tackling this scaling gap.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1045-9227
1941-0093
1941-0093
DOI:10.1109/TNN.2011.2134109