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
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
More filters
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
More filters
Journal ArticleDOI
2005 Special Issue: Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Alex Graves,Jürgen Schmidhuber +1 more
TL;DR: In this article, a modified, full gradient version of the LSTM learning algorithm was used for framewise phoneme classification, using the TIMIT database, and the results support the view that contextual information is crucial to speech processing, and suggest that bidirectional networks outperform unidirectional ones.
End to end speech recognition in English and Mandarin
Dario Amodei,Rishita Anubhai,Eric Battenberg,Carl Case,Jared Casper,Bryan Catanzaro,Jingdong Chen,Mike Chrzanowski,Adam Coates,Greg Diamos,Erich Elsen,Jesse Engel,Linxi Fan,Christopher Fougner,Tony X. Han,Awni Hannun,Billy Jun,Patrick LeGresley,Libby Lin,Sharan Narang,Andrew Y. Ng,Sherjil Ozair,Ryan Prenger,Jonathan Raiman,Sanjeev Satheesh,David Seetapun,Shubho Sengupta,Yi Wang,Zhiqian Wang,Chong Wang,Bo Xiao,Dani Yogatama,Jun Zhan,Zhenyao Zhu +33 more
TL;DR: It is shown that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages, and is competitive with the transcription of human workers when benchmarked on standard datasets.
Posted Content
Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun,Carl Case,Jared Casper,Bryan Catanzaro,Greg Diamos,Erich Elsen,Ryan Prenger,Sanjeev Satheesh,Shubho Sengupta,Adam Coates,Andrew Y. Ng +10 more
TL;DR: Deep Speech, a state-of-the-art speech recognition system developed using end-to-end deep learning, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set.
Proceedings ArticleDOI
DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning
TL;DR: This study designs an accelerator for large-scale CNNs and DNNs, with a special emphasis on the impact of memory on accelerator design, performance and energy, and shows that it is possible to design an accelerator with a high throughput, capable of performing 452 GOP/s in a small footprint.
Journal ArticleDOI
Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks
TL;DR: A novel dataflow, called row-stationary (RS), is presented, that minimizes data movement energy consumption on a spatial architecture and can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine local storage, direct inter-PE communication and spatial parallelism.
Related Papers (5)
In-Datacenter Performance Analysis of a Tensor Processing Unit
Norman P. Jouppi,Cliff Young,Nishant Patil,David A. Patterson,Gaurav Agrawal,Raminder Bajwa,Sarah Bates,Suresh Bhatia,Nan Boden,Albert T. Borchers,Rick Boyle,Pierre-luc Cantin,Clifford Chao,Christopher Aaron Clark,Jeremy Coriell,Michael J. Daley,Matt Dau,Jeffrey Dean,Ben Gelb,Tara Vazir Ghaemmaghami,Rajendra Gottipati,William John Gulland,Robert Hagmann,C. Richard Ho,Doug Hogberg,John Hu,Robert Hundt,D. Hurt,Julian Ibarz,Aaron Jaffey,Alek Jaworski,Alexander Kaplan,Khaitan Harshit,Daniel Killebrew,Andy Koch,Naveen Kumar,Steve Lacy,James Laudon,James Law,Diemthu Le,Chris Leary,Zhuyuan Liu,Kyle Lucke,Alan Lundin,Gordon MacKean,Adriana Maggiore,Maire Mahony,Kieran Miller,Rahul Nagarajan,Ravi Narayanaswami,Ray Ni,Kathy Nix,Thomas Norrie,Mark Omernick,Narayana Penukonda,Andrew Everett Phelps,Jonathan Ross,Matt Ross,Amir Salek,Emad Samadiani,Chris Severn,Gregory Sizikov,Matthew Snelham,Jed Souter,Dan Steinberg,Andy Swing,Mercedes Tan,Gregory Michael Thorson,Bo Tian,Horia Toma,Erick Tuttle,Vijay K. Vasudevan,Richard Walter,Walter Wang,Eric Wilcox,Doe Hyun Yoon +75 more