This title appears in the Scientific Report :
2012
Please use the identifier:
http://dx.doi.org/10.3389/fnins.2012.00090 in citations.
Please use the identifier: http://hdl.handle.net/2128/4890 in citations.
Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware
Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware
Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modificat...
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Personal Name(s): | Pfeil, Thomas (Corresponding author) |
---|---|
Potjans, Tobias Christian / Schrader, Sven / Potjans, Wiebke / Schemmel, Johannes / Diesmann, Markus / Meier, Karlheinz | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 |
Published in: | Frontiers in neuroscience, 6 (2012) 90, S. 1-19 |
Imprint: |
Lausanne
Frontiers Research Foundation
2012
|
DOI: |
10.3389/fnins.2012.00090 |
PubMed ID: |
22822388 |
Document Type: |
Journal Article |
Research Program: |
Theory, modelling and simulation Helmholtz Alliance on Systems Biology Fast Analog Computing with Emergent Transient States - Initial Training Network (FACETS-ITN) Brain-inspired multiscale computation in neuromorphic hybrid systems Signalling Pathways and Mechanisms in the Nervous System |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/4890 in citations.
Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modification that saves resources would be reducing synaptic weight resolution. In this study, we give an estimate for the impact of synaptic weight discretization on different levels, ranging from random walks of individual weights to computer simulations of spiking neural networks. The FACETS wafer-scale hardware system offers a 4-bit resolution of synaptic weights, which is shown to be sufficient within the scope of our network benchmark. Our findings indicate that increasing the resolution may not even be useful in light of further restrictions of customized mixed-signal synapses. In addition, variations due to production imperfections are investigated and shown to be uncritical in the context of the presented study. Our results represent a general framework for setting up and configuring hardware-constrained synapses. We suggest how weight discretization could be considered for other backends dedicated to large-scale simulations. Thus, our proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists. |