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检索条件"主题词=computing operators"

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Space and Time Optimal DNN Primitive Selection with Integer Linear Programming 19

Space and Time Optimal DNN Primitive Selection with Integer ...

引用

Proceedings of the International Conference on Parallel Architectures and Compilation Techniques

作者： Yuan Wen Andrew Anderson Valentin Radu Michael F.P. O'Boyle David Gregg Trinity College Dublin University of Edinburgh

ISBN: (纸本)9781728136134

Convolutional neural networks (CNNs) are used in many applications, from industrial robotics to biometric identification on mobile devices. But they can be too resource-hungry for mobile and embedded devices with tightly constrained memory and energy budgets. We propose an ahead-of-time primitive selection for CNNs, based on integer linear programming (ILP). Under a tight memory budget, our ILP solver selects the optimal primitive for each layer such that the entire network is optimized for execution time subject to a memory budget, or vice versa. Our method yields significant speedup and memory reduction compared to existing methods.

关键词： computing operators

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TASO: Time and Space Optimization for Memory-Constrained DNN Inference 32

TASO: Time and Space Optimization for Memory-Constrained DNN...

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32nd IEEE International Symposium on Computer Architecture and High-Performance computing (SBAC-PAD) / 11th Workshop on Applications for Multi-Core Architectures (WAMCA)

作者： Wen, Yuan Anderson, Andrew Radu, Valentin O'Boyle, Michael F. P. Gregg, David Trinity Coll Dublin Dublin Ireland Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781728199245

Convolutional neural networks (CNNs) are used in many embedded applications, from industrial robotics and automation systems to biometric identification on mobile devices. State-of-the-art classification is typically achieved by large networks, which are prohibitively expensive to run on mobile and embedded devices with tightly constrained memory and energy budgets. We propose an approach for ahead-of-time domain-specific optimization of CNN models, based on an integer linear programming (ILP) for selecting primitive operations to implement convolutional layers. We optimize the trade-off between execution time and memory consumption by: 1) attempting to minimize execution time across the whole network by selecting data layouts and primitive operations to implement each layer;and 2) allocating an appropriate workspace that reflects the upper bound of memory footprint per layer. These two optimization strategies can be used to run any CNN on any platform with a C compiler. Our evaluation with a range of popular ImageNet neural architectures (GoogleNet, AlexNet, VGG, ResNet and SqueezeNet) on the ARM Cortex-A15 yields speedups of 8x compared to a greedy algorithm based primitive selection, reduces memory requirement by 2.2x while sacrificing only 15% of inference time compared to a solver that considers inference time only. In addition, our optimization approach exposes a range of optimal points for different configurations across the Pareto frontier of memory and latency trade-off, which can be used under arbitrary system constraints.

关键词： neural network optimization computing operators primitive selection optimal convolutional layer memory-time trade-off

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Poster: Space and Time Optimal DNN Primitive Selection with Integer Linear Programming 28

Poster: Space and Time Optimal DNN Primitive Selection with ...

引用

28th International Conference on Parallel Architectures and Compilation Techniques (PACT)

作者： Wen, Yuan Anderson, Andrew Radu, Valentin O'Boyle, Michael F. P. Gregg, David Trinity Coll Dublin Dublin Ireland Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781728136134

Convolutional neural networks (CNNs) are used in many applications, from industrial robotics to biometric identification on mobile devices. But they can be too resource-hungry for mobile and embedded devices with tightly constrained memory and energy budgets. We propose an aheadof-time primitive selection for CNNs, based on integer linear programming (ILP). Under a tight memory budget, our ILP solver selects the optimal primitive for each layer such that the entire network is optimized for execution time subject to a memory budget, or vice versa. Our method yields significant speedup and memory reduction compared to existing methods.

关键词： neural network optimization computing operators primitive selection optimal convolutional layer

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