A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

IEEE Transactions on Circuits and Systems I: Regular Papers

Volumn 65, Issue 1, 2018, Pages 198-208

  Du, Li ^a,b   Du, Yuan ^a,b   Li, Yilei ^c   Su, Junjie ^b   Kuan, Yen Cheng ^d   Liu, Chun Chen ^a,b   Chang, Mau Chung Frank ^a,d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Kneron   (United States)

^c NOVUMIND INC   (United States)

^d NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Convolution neural network; deep learning; hardware accelerator; IoT

Indexed keywords

ACCELERATION; COMPUTER HARDWARE; CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DEEP LEARNING; DEEP NEURAL NETWORKS; ENERGY EFFICIENCY;

65-NM TECHNOLOGIES; CONVOLUTION NEURAL NETWORK; CONVOLUTION WINDOWS; FILTER DECOMPOSITION; HARDWARE ACCELERATORS; INTERNET OF THING (IOT); STREAMING HARDWARES; THROUGHPUT IMPROVEMENT;

INTERNET OF THINGS;

EID: 85028457260     PISSN: 15498328     EISSN: 15580806     Source Type: Journal    
DOI: 10.1109/TCSI.2017.2735490     Document Type: Article

References (20)

1. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436-444, May 2015.
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., vol. 25. 2012, pp. 1097-1105.
3. D. Silver et al., "Mastering the game of Go with deep neural networks and tree search," Nature, vol. 529, no. 7587, pp. 484-489, Jan. 2016.
4. S. Bilac. How to Classify Images With Tensor Flow Using Google Cloud Machine Learning and Cloud Dataflow. Accessed on Nov. 1, 2016. [Online]. Available: https://cloud.google.com/blog/big-data/2016/12/how-to-classify-images-with-tensorflow-using-google-cloud-machinelearning-and-cloud-dataflow
5. Amazon Rekognition. Accessed on Nov. 1, 2016. [Online]. Available: https://aws.amazon.com/rekognition/
6. K. Simonyan and A. Zisserman, "Very deep convolution networks for large-scale image recognition," CoRR, pp. 1-14, Sep. 2014.
7. R. Hameed et al., "Understanding sources of inefficiency in general-purpose chips," in Proc. 37th Annu. Int. Symp. Comput. Archit., 2010, pp. 37-47.
8. M. Horowitz, "Computing's energy problem (and what we can do about it)," in IEEE Int. Solid-State Circuits Conf. (ISSCC) Dig. Tech. Papers, Feb. 2014, pp. 10-14.
9. S. Han et al., "EIE: Efficient inference engine on compressed deep neural network," in Proc. 43rd Int. Symp. Comput. Archit., 2016, pp. 243-254.
10. Y.-H. Chen, T. Krishna, J. S. Emer, and V. Sze, "Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks," in IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers (ISSCC), San Francisco, CA, USA, Jan./Feb. 2016, pp. 262-263.
11. C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, "Optimizing FPGA-based accelerator design for deep convolution neural networks," in Proc. ACM/SIGDA Int. Symp. Field-Program. Gate Arrays, 2015, pp. 161-170.
12. J. Sim, J.-S. Park, M. Kim, D. Bae, Y. Choi, and L.-S. Kim, "A 1.42TOPS/W deep convolution neural network recognition processor for intelligent IoE systems," in IEEE Int. Solid-State Circuits Conf. (ISSCC) Dig. Tech. Papers, Jan./Feb. 2016, pp. 264-265.
13. A. Shafiee et al., "ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars," in Proc. 43rd Int. Symp. Comput. Archit., 2016, pp. 14-26.
14. M. D. Pickett, "The materials science of titanium dioxide memristors," Ph.D. dissertation, Dept. Mater. Sci. Eng., Univ. California, Berkeley, Berkeley, CA, USA, 2010.
15. Y. Zeng, X. Xu, Y. Fang, and K. Zhao, "Traffic sign recognition using extreme learning classifier with deep convolution features," in Proc. Int. Conf. Intell. Sci. Big Data Eng. (IScIDE), Suzhou, China, 2015, pp. 1-10.
16. A. Krizhevsky, I. Sutskever, and G. Hinton, "Image Net classification with deep convolution neural networks," in Proc. Conf. Workshop Neural Inf. Process. Syst., 2012, pp. 1106-1114.
17. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770-778.
18. S. Gupta, A. Agrawal, K. Gopalakrishnan, and P. Narayanan, "Deep learning with limited numerical precision," in Proc. 32nd Int. Conf. Int. Conf. Mach. Learn. (ICML), vol. 37. Lille, France, 2015, pp. 1737-1746.
19. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
20. Kneron Inc. Hardware IP Demo. Accessed on Jun. 1, 2017. [Online]. Available: https://www.youtube.com/watch?v=ttdSLXmBEWE