Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing

27 February 2016

Papers citing "Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing"

8 / 8 papers shown

Title
Approximate Computing and the Efficient Machine Learning Expedition J. Henkel Hai Helen Li A. Raghunathan M. Tahoori Swagath Venkataramani Xiaoxuan Yang Georgios Zervakis 28 17 0 02 Oct 2022
The Effects of Approximate Multiplication on Convolutional Neural Networks Min Soo Kim A. D. Del Barrio Hyunjin Kim N. Bagherzadeh 18 46 0 20 Jul 2020
Automated Circuit Approximation Method Driven by Data Distribution Z. Vašíček Vojtěch Mrázek Lukás Sekanina 15 17 0 11 Mar 2019
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators Jeff Zhang Kartheek Rangineni Zahra Ghodsi S. Garg 36 118 0 11 Feb 2018
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks Ruizhou Ding Z. Liu Rongye Shi Diana Marculescu R. D. Blanton MQ 24 37 0 02 Dec 2017
A Survey of Neuromorphic Computing and Neural Networks in Hardware Catherine D. Schuman T. Potok Robert M. Patton J. Birdwell Mark E. Dean Garrett S. Rose J. Plank 54 687 0 19 May 2017
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks Syed Shakib Sarwar Priyadarshini Panda Kaushik Roy CVBM 24 100 0 12 May 2017
Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural Networks Hokchhay Tann S. Hashemi Iris Bahar Sherief Reda MQ 35 74 0 11 May 2017