Experimental Demonstration Of The Fixed-Point Sparse Coding Performance
By: Jingfei Jiang, Rongdong Hu, Fei Zhang and Yong Dou

References
- 1. Hinton, G., S. Osindero, Y. T h e. A Fast Learning Algorithm for Deep Belief Nets. - Neural Computation, Vol. 18, 2006, No 7, 1527-1554.
- 2. Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio, P. Manzagol. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. - The Journal of Machine Learning Research, Vol. 11, 2010, 3371-3408.
- 3. Lee, H., C. Ekanadham, A. Ng. Sparse Deep Belief Net Model for Visual Area V2. - Advances in Neural Information Processing Systems, Vol. 20, 2008, 873-880.
- 4. Nasse, F., C. Thurau, G. Fink. Face Detection Using Gpu-Based Convolutional Neural Networks. - Computer Analysis of Images and Patterns, 2009, 83-90.
- 5. Le, Q., R. Monga, M. Devin, G. Corrado, K. Chen, M. Ranzato, J. Dean, A. Ng. Building High-Level Features Using Large Scale Unsupervised Learning. - ArXiv Preprint ArXiv:1112.6209, 2011.
- 6. Raina, R., A. Madhavan, A. Ng. Large-Scale Deep Unsupervised Learning Using Graphics Processors. - In: Proc. of 26th Annual International Conference on Machine Learning, Vol. 382, ACM, 2009, 873-880.
- 7. Ly, D., P. Chow. A Multi-fpga Architecture for Stochastic Restricted Boltzmann Machines, in Field Programmable Logic and Applications. 2009. FPL 2009. - In: International Conference on. IEEE, 2009, 168-173.
- 8. Chen, Tianshi, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, Olivier Temam. DianNao: A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning. - In: Proc. of 19th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS’14), 2014.
- 9. Savich, A., M. Moussa, S. Areibi. The Impact of Arithmetic Representation on Implementing mlp-bp on fpgas: A Study. - IEEE Transactions on Neural Networks, Vol. 18, 2007, No 1, 240-252.
- 10. Draghici, S. On the Capabilities of Neural Networks Using Limited Precision Weights. - Neural Networks, Vol. 15, 2002, No 3, 395-414.
- 11. Savich, A., M. Moussa. Resource Efficient Arithmetic Effects on rbm Neural Network Solution Quality Using mnist. - In: International Conference on Reconfigurable Computing and FPGAs, Cancun, Mexico, 30 November - 2 December 2011, 35-40.
- 12. Jiang, Jingfei, Rongdong Hu, et al. Accuracy Evaluation of Deep Belief Networks with Fixed-Point Arithmetic. - Computer Modelling & New Technologies, Vol. 6, 2014.
- 13. Kim, S., P. McMahon, K. Olukotun. A Large-Scale Architecture for Restricted Boltzmann Machines. - In: Proc. of 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, Charlotte, North Carolina, 2-4 May 2010, 201-208.
- 14. Le, Ly D., P. Chow. High-Performance Reconfigurable Hardware Architecture for Restricted Boltzmann Machines. - IEEE Transactions on Neural Networks, Vol. 21, 2010, No 11, 1780-1792.
- 15. Lee, H., A. Battle, R. Raina et al. Efficient Sparse Coding Algorithms. - Advances in Neural Information Processing Systems, 2006, 801-808.
- 16. Hosmer, JrD. W., S. Lemeshow. Applied Logistic Regression. John Wiley & Sons, 2004.
DOI: https://doi.org/10.2478/cait-2014-0042 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702 (formerly 1314-4081)
Language: English
Page range: 40 - 50
Published on: Dec 30, 2014
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2014 Jingfei Jiang, Rongdong Hu, Fei Zhang, Yong Dou, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.