On the Impact of Device-Level Techniques on Energy-Efficiency of Neural Network Accelerators

26 June 2021

Papers citing "On the Impact of Device-Level Techniques on Energy-Efficiency of Neural Network Accelerators"

8 / 8 papers shown

Title
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration Behzad Salami Erhan Baturay Onural Ismail Emir Yüksel Fahrettin Koc Oguz Ergin A. Cristal O. Unsal H. Sarbazi-Azad O. Mutlu 44 45 0 04 May 2020
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators Jeff Zhang Kartheek Rangineni Zahra Ghodsi S. Garg 55 118 0 11 Feb 2018
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks Hardik Sharma Jongse Park Naveen Suda Liangzhen Lai Benson Chau Joo-Young Kim Vikas Chandra H. Esmaeilzadeh MQ 55 490 0 05 Dec 2017
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang Xinyu Zhou Mengxiao Lin Jian Sun AI4TS 138 6,867 0 04 Jul 2017
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks A. Parashar Minsoo Rhu Anurag Mukkara A. Puglielli Rangharajan Venkatesan Brucek Khailany J. Emer S. Keckler W. Dally 73 1,126 0 23 May 2017
TensorFlow: A system for large-scale machine learning Martín Abadi P. Barham Jianmin Chen Zhiwen Chen Andy Davis ... Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu Xiaoqiang Zhang GNN AI4CE 429 18,350 0 27 May 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,525 0 01 Sep 2014
Caffe: Convolutional Architecture for Fast Feature Embedding Yangqing Jia Evan Shelhamer Jeff Donahue Sergey Karayev Jonathan Long Ross B. Girshick S. Guadarrama Trevor Darrell VLM BDL 3DV 271 14,710 0 20 Jun 2014