Compact Convolution Mapping on Neuromorphic Hardware using Axonal Delay
- Title
- Compact Convolution Mapping on Neuromorphic Hardware using Axonal Delay
- Authors
- KIM, Jinseok; KIM, Yulhwa; KIM, Sungho; KIM, JAE JOON
- Date Issued
- 2018-07-23
- Publisher
- ACM/IEEE
- Abstract
- Mapping Convolutional Neural Network (CNN) to a neuromorphic
hardware has been inefficient in synapse memory usage because
both kernel/input reuse are not exploitedwell.We propose a method
to enable kernel reuse by utilizing axonal delay, which is a biological
parameter for a spiking neuron. Using IBM TrueNorth as a
test platform, we demonstrate that the number of cores, neurons,
synapses, and synaptic operations per time step can be reduced by
up to 20.9×, 27.9×, 88.4×, and 1586×, respectively, compared to the
conventional scheme, which raises the possibility of implementing
large-scale CNN on neuromorphic hardware.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/97842
- Article Type
- Conference
- Citation
- International Symposium on Low Power Electronics and Design (ISLPED), 2018-07-23
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- There are no files associated with this item.
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