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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Proceedings ArticleDOI
Deeper weight pruning without accuracy loss in deep neural networks
Byungmin Ahn,Taewhan Kim +1 more
TL;DR: This work proposes a transformation technique which converts the two’s complement representation of every weight into a set of CSD representations of the minimal or near-minimal number of essential bits, and proposes a supporting novel acceleration architecture with no additional inclusion of non-trivial hardware.
Journal ArticleDOI
nZESPA: A Near-3D-Memory Zero Skipping Parallel Accelerator for CNNs
Palash Das,Hemangee K. Kapoor +1 more
TL;DR: NMP-fully sparse architecture is introduced, which acquires all three capabilities of sparsity, parallel and hence processes the independent CNN tasks concurrently and outperforms the baselines in terms of performance and energy consumption while executing CNN inference.
Book ChapterDOI
Performance Improvements in Quantization Aware Training and Appreciation of Low Precision Computation in Deep Learning
TL;DR: This paper attempts at giving a roadmap in designing better deep learning systems by considering reducing memory footprint and latency while deploying DNN in resource constraint devices (edge devices) using QAT.
Posted Content
Optimizing GPU Cache Policies for MI Workloads
Johnathan Alsop,Matthew D. Sinclair,Srikant Bharadwaj,Alexandru Dutu,Anthony Gutierrez,Onur Kayiran,Michael LeBeane,Sooraj Puthoor,Xianwei Zhang,Tsung Tai Yeh,Bradford M. Beckmann +10 more
TL;DR: In this paper, the authors evaluate 17 machine intelligence (MI) applications and characterize their behaviors using a range of GPU caching strategies, and find that the choice of caching policy in GPU caches involves multiple performance trade-offs and interactions, and there is no one-size-fits-all GPU caching policy for MI workloads.
Posted Content
Activation Density driven Energy-Efficient Pruning in Training
TL;DR: A novel pruning method that prunes a network real-time during training, reducing the overall training time to achieve an efficient compressed network and introducing an activation density based analysis to identify the optimal relative sizing or compression for each layer of the network.
References
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Proceedings ArticleDOI
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
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