Yue Wang

According to our database1, Yue Wang authored at least 12 papers between 2018 and 2020.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 



On csauthors.net:


Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference.
IEEE J. Sel. Top. Signal Process., 2020

A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision.
CoRR, 2020

SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation.
Proceedings of the 47th ACM/IEEE Annual International Symposium on Computer Architecture, 2020

Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

DNN-Chip Predictor: An Analytical Performance Predictor for DNN Accelerators with Various Dataflows and Hardware Architectures.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs.
Proceedings of the FPGA '20: The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2020

Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving.
CoRR, 2019

Drawing early-bird tickets: Towards more efficient training of deep networks.
CoRR, 2019

E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Live Demonstration: Bringing Powerful Deep Learning into Daily-Life Devices (Mobiles and FPGAs) Via Deep k-Means.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2019

Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions.
Proceedings of the 35th International Conference on Machine Learning, 2018