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
Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
TL;DR: Eyeriss v2 as mentioned in this paper is a DNN accelerator architecture designed for running compact and sparse DNNs, which can process sparse data directly in the compressed domain for both weights and activations and therefore is able to improve both processing speed and energy efficiency with sparse models.
Journal ArticleDOI
Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey
TL;DR: This article reviews the mainstream compression approaches such as compact model, tensor decomposition, data quantization, and network sparsification, and answers the question of how to leverage these methods in the design of neural network accelerators and present the state-of-the-art hardware architectures.
Proceedings ArticleDOI
A configurable cloud-scale DNN processor for real-time AI
Jeremy Fowers,Kalin Ovtcharov,Michael K. Papamichael,Todd Massengill,Ming Liu,Lo Daniel,Shlomi Alkalay,Michael Haselman,Logan Adams,Mahdi Ghandi,Stephen F. Heil,Prerak Patel,Adam Sapek,Gabriel Weisz,Lisa Woods,Sitaram Lanka,Steven K. Reinhardt,Adrian M. Caulfield,Eric S. Chung,Doug Burger +19 more
TL;DR: This paper describes the NPU architecture for Project Brainwave, a production-scale system for real-time AI, and achieves more than an order of magnitude improvement in latency and throughput over state-of-the-art GPUs on large RNNs at a batch size of 1.5 teraflops.
Proceedings ArticleDOI
Bit fusion: bit-level dynamically composable architecture for accelerating deep neural networks
Hardik Sharma,Jongse Park,Naveen Suda,Liangzhen Lai,Benson Chau,Vikas Chandra,Hadi Esmaeilzadeh +6 more
TL;DR: This work designs Bit Fusion, a bit-flexible accelerator that constitutes an array of bit-level processing elements that dynamically fuse to match the bitwidth of individual DNN layers, and compares it to two state-of-the-art DNN accelerators, Eyeriss and Stripes.
Posted Content
Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices
TL;DR: Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs, is presented, which introduces a highly flexible on-chip network that can adapt to the different amounts of data reuse and bandwidth requirements of different data types, which improves the utilization of the computation resources.
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
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Proceedings Article
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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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