Proceedings ArticleDOI
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Angshuman Parashar,Minsoo Rhu,Anurag Mukkara,Antonio Puglielli,Rangharajan Venkatesan,Brucek Khailany,Joel Emer,Stephen W. Keckler,William J. Dally +8 more
- Vol. 45, Iss: 2, pp 27-40
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TLDR
The Sparse CNN (SCNN) accelerator as discussed by the authors employs a dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements.Abstract:
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.read more
Citations
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Journal ArticleDOI
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
TL;DR: In this paper, the authors provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs, and discuss various hardware platforms and architectures that support DNN, and highlight key trends in reducing the computation cost of deep neural networks either solely via hardware design changes or via joint hardware and DNN algorithm changes.
Book ChapterDOI
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
TL;DR: This paper proposes AutoML for Model Compression (AMC) which leverages reinforcement learning to efficiently sample the design space and can improve the model compression quality and achieves state-of-the-art model compression results in a fully automated way without any human efforts.
Posted Content
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
TL;DR: In this article, the authors provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs, and discuss various hardware platforms and architectures that support deep neural networks.
Posted Content
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael H. Zhu,Suyog Gupta +1 more
TL;DR: In this article, the authors investigate two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning.
Posted Content
AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
TL;DR: This paper proposed AutoML for Model Compression (AMC) which leverages reinforcement learning to provide the model compression policy, which outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor.
References
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Posted Content
Accelerating Deep Convolutional Networks using low-precision and sparsity
TL;DR: This work achieves the highest reported accuracy with extremely low-precision (2-bit) weight networks and builds a deep learning accelerator core, DLAC, that can achieve up to 1 TFLOP/mm2 equivalent for single- Precision floating-point operations.
Proceedings Article
Persistent RNNs: stashing recurrent weights on-chip
Gregory Diamos,Shubho Sengupta,Bryan Catanzaro,Mike Chrzanowski,Adam Coates,Erich Elsen,Jesse Engel,Awni Hannun,Sanjeev Satheesh +8 more
TL;DR: This paper introduces a new technique for mapping Deep Recurrent Neural Networks efficiently onto GPUs that uses persistent computational kernels that exploit the GPU's inverted memory hierarchy to reuse network weights over multiple timesteps.
Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
TL;DR: In this article, the authors proposed a method to improve the performance of the YFA-funded DARPA YFA grant N66001-14-1-4039 (DARPA-YFA-14/1/4039).
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