Deep Learning Accelerator (Convolution Neural Networks)
This is an implementation of MIT Eyeriss-like deep learning accelerator in Verilog
Note: clacc stands for convolutional layer accelerator
This is originally a course project of Deep Learning Hardware Accelerator Design at National Tsing Hua University, lectured by Prof. Youn-Long Lin. The course is an equivalent of CS231n from stanford.
- The Eyeriss project: https://eyeriss.mit.edu/
- Y.-H. Chen, T. Krishna, J. Emer, V. Sze, "Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks," IEEE Journal of Solid State Circuits (JSSC), ISSCC Special Issue, Vol. 52, No. 1, pp. 127-138, January 2017.
- Y.-H. Chen, J. Emer, V. Sze, "Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks," International Symposium on Computer Architecture (ISCA), pp. 367-379, June 2016.
- Y.-H. Chen, T. Krishna, J. Emer, V. Sze, "Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks," IEEE International Conference on Solid-State Circuits (ISSCC), pp. 262-264, February 2016.
- Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015. Deep learning with limited numerical precision. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (ICML'15), Francis Bach and David Blei (Eds.), Vol. 37. JMLR.org 1737-1746.
MIT License
Copyright (c) 2017 Michael (Tao-Yi) Lee
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