Publisher
Springer Nature Switzerland
Reference16 articles.
1. Anderson, A., Vasudevan, A., Keane, C., Gregg, D.: High-performance low-memory lowering: GEMM-based algorithms for DNN convolution. In: SBAC-PAD 2020, pp. 99–106 (2020). https://doi.org/10.1109/SBAC-PAD49847.2020.00024
2. Chellapilla, K., Puri, S., Simard, P.: High-performance convolutional neural networks for document processing. In: IWFHR 2006, pp. 99–106 (2006)
3. Chen, G., et al.: 16.1 A 340mV-to-0.9V 20.2Tb/s source-synchronous hybrid packet/circuit-switched 16$$\times $$16 network-on-chip in 22nm tri-gate CMOS. In: ISSCC 2014, pp. 276–277 (2014). https://doi.org/10.1109/ISSCC.2014.6757432
4. Cho, M., Brand, D.: MEC: memory-efficient convolution for deep neural network. In: ICML 2017, vol. 70, pp. 815–824 (2017)
5. Dongarra, J.J., Du Croz, J., Hammarling, S., Duff, I.S.: A set of level 3 basic linear algebra subprograms. ACM Trans. Math. Softw. 16(1), 1–17 (1990). https://doi.org/10.1145/77626.79170