1 |
J. Lee, C. Kim, S. Kang, D. Shin, S. Kim, and H.-J. Yoo, "UNPU: A 50.6 TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision," in 2018 IEEE International Solid-State Circuits Conference-(ISSCC), 2018: IEEE, pp. 218-220.
|
2 |
V. Mnih et al., "Human-level control through deep reinforcement learning," in Nature, vol. 518, no. 7540, p. 529, 2015.
DOI
|
3 |
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. J. a. p. a. Klimov, "Proximal policy optimization algorithms," 2017.
|
4 |
K. Ueyoshi et al., "QUEST: A 7.49 TOPS multi-purpose logquantized DNN inference engine stacked on 96MB 3D SRAM using inductive-coupling technology in 40nm CMOS," in 2018 IEEE International Solid-State Circuits Conference-(ISSCC), 2018: IEEE, pp. 216-218.
|
5 |
V. Mnih et al., "Playing atari with deep reinforcement learning," arXiv preprint arXiv:1312.5602, 2013.
|
6 |
Z. Yuan et al., "STICKER: A 0.41-62.1 TOPS/W 8bit Neural Network Processor with Multi-Sparsity Compatible Convolution Arrays and Online Tuning Acceleration for Fully Connected Layers," in 2018 IEEE Symposium on VLSI Circuits, 2018: IEEE, pp. 33-34.
|