The memory requirement of deep learning algorithms is considered incompatible with the memory restriction of energy-efficient hardware. A low memory footprint can be achieved by pruning obsolete connections or reducing the precision of connection strengths after the network has been trained. Yet, these techniques are not applicable to the case when neural networks have to be trained directly on hardware due to the hard memory constraints. Deep Rewiring (DEEP R) is a training algorithm which continuously rewires the network while preserving very sparse connectivity all along the training procedure. We apply DEEP R to a deep neural network implementation on a prototype chip of the 2nd generation SpiNNaker system. The local memory of a single core on this chip is limited to 64 KB and a deep network architecture is trained entirely within this constraint without the use of external memory. Throughout training, the proportion of active connections is limited to 1.3\%. On the handwritten digits dataset MNIST, this extremely sparse network achieves 96.6\% classification accuracy at convergence. Utilizing the multi-processor feature of the SpiNNaker system, we found very good scaling in terms of computation time, per-core memory consumption, and energy constraints. When compared to a X86 CPU implementation, neural network training on the SpiNNaker 2 prototype improves power and energy consumption by two orders of magnitude.
%0 Journal Article
%1 Liu2018-li
%A Liu, Chen
%A Bellec, Guillaume
%A Vogginger, Bernhard
%A Kappel, David
%A Partzsch, Johannes
%A Neumärker, Felix
%A Höppner, Sebastian
%A Maass, Wolfgang
%A Furber, Steve B
%A Legenstein, Robert
%A Mayr, Christian G
%D 2018
%I Frontiers Media SA
%J Front. Neurosci.
%K SpiNNaker; deep efficient energy footprint; hardware; memory parallelism; pruning; rewiring; sparsity
%P 840
%T Memory-efficient deep learning on a SpiNNaker 2 prototype
%V 12
%X The memory requirement of deep learning algorithms is considered incompatible with the memory restriction of energy-efficient hardware. A low memory footprint can be achieved by pruning obsolete connections or reducing the precision of connection strengths after the network has been trained. Yet, these techniques are not applicable to the case when neural networks have to be trained directly on hardware due to the hard memory constraints. Deep Rewiring (DEEP R) is a training algorithm which continuously rewires the network while preserving very sparse connectivity all along the training procedure. We apply DEEP R to a deep neural network implementation on a prototype chip of the 2nd generation SpiNNaker system. The local memory of a single core on this chip is limited to 64 KB and a deep network architecture is trained entirely within this constraint without the use of external memory. Throughout training, the proportion of active connections is limited to 1.3\%. On the handwritten digits dataset MNIST, this extremely sparse network achieves 96.6\% classification accuracy at convergence. Utilizing the multi-processor feature of the SpiNNaker system, we found very good scaling in terms of computation time, per-core memory consumption, and energy constraints. When compared to a X86 CPU implementation, neural network training on the SpiNNaker 2 prototype improves power and energy consumption by two orders of magnitude.
@article{Liu2018-li,
abstract = {The memory requirement of deep learning algorithms is considered incompatible with the memory restriction of energy-efficient hardware. A low memory footprint can be achieved by pruning obsolete connections or reducing the precision of connection strengths after the network has been trained. Yet, these techniques are not applicable to the case when neural networks have to be trained directly on hardware due to the hard memory constraints. Deep Rewiring (DEEP R) is a training algorithm which continuously rewires the network while preserving very sparse connectivity all along the training procedure. We apply DEEP R to a deep neural network implementation on a prototype chip of the 2nd generation SpiNNaker system. The local memory of a single core on this chip is limited to 64 KB and a deep network architecture is trained entirely within this constraint without the use of external memory. Throughout training, the proportion of active connections is limited to 1.3\%. On the handwritten digits dataset MNIST, this extremely sparse network achieves 96.6\% classification accuracy at convergence. Utilizing the multi-processor feature of the SpiNNaker system, we found very good scaling in terms of computation time, per-core memory consumption, and energy constraints. When compared to a X86 CPU implementation, neural network training on the SpiNNaker 2 prototype improves power and energy consumption by two orders of magnitude.},
added-at = {2024-09-10T11:54:51.000+0200},
author = {Liu, Chen and Bellec, Guillaume and Vogginger, Bernhard and Kappel, David and Partzsch, Johannes and Neum{\"a}rker, Felix and H{\"o}ppner, Sebastian and Maass, Wolfgang and Furber, Steve B and Legenstein, Robert and Mayr, Christian G},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/232c7b4c91d514946a8e26bf154888da1/scadsfct},
interhash = {aa9b793eea12043de98bc65fdc6934e5},
intrahash = {32c7b4c91d514946a8e26bf154888da1},
journal = {Front. Neurosci.},
keywords = {SpiNNaker; deep efficient energy footprint; hardware; memory parallelism; pruning; rewiring; sparsity},
language = {en},
month = nov,
pages = 840,
publisher = {Frontiers Media SA},
timestamp = {2024-09-10T11:54:51.000+0200},
title = {Memory-efficient deep learning on a {SpiNNaker} 2 prototype},
volume = 12,
year = 2018
}
