Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning

2017 5th Berkeley Symposium on Energy Efficient Electronic Systems, E3S 2017 - Proceedings

Volumn 2018-January, Issue , 2017, Pages 1-3

  Bojnordi, Mahdi Nazm ^a   Ipek, Engin ^b  

^a UNIVERSITY OF UTAH   (United States)

^b UNIVERSITY OF ROCHESTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL OPTIMIZATION; DATA MINING; ENERGY EFFICIENCY; HEURISTIC ALGORITHMS; INTEGER PROGRAMMING;

APPROXIMATE SOLUTION; BOLTZMANN MACHINES; CLASSICAL PROBLEMS; HARDWARE ACCELERATORS; INTEGER LINEAR PROGRAMMING; OPTIMIZATION PROBLEMS; SCIENCE AND ENGINEERING; TRAVELING SALESMAN;

DEEP LEARNING;

EID: 85045997260     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/E3S.2017.8246178     Document Type: Conference Paper

References (4)

1. I. Wegener, "Simulated annealing beats metropolis in combinatorial optimization, " Electronic Colloquium on Computational Complexity, 2004
2. S. E. Fahlman, G. E. Hinton, and T. J. Sejnowski, "Massively parallel architectures for AI: NETL, Thistle, and boltzmann machines, " in Proceedings of Association for -The Advancement of Artificial Intelligence (AAAI), pp. 109-113, 1983
3. U. D. of Energy, "The top ten exascale research challenges, " Report of -The Advanced Scientific Computing Advisory Committee Subcommittee, 2014
4. A. Shafiee, A. Nag, N. Muralimanohar, R. Balasubramonian, J. P. Strachan, M. Hu, R. S. Williams, and V. Srikumar, "Isaac: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars, " in Proc. ISCA, 2016.