Sze, Vivienne

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 4,215

Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Chen, Y.-H., Krishna, T., Emer, J. S., & Sze, V. (2017) · IEEE Journal of Solid-State Circuits

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 3,247

Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

Chen, Y.-H., Emer, J., & Sze, V. (2016) · ACM SIGARCH Computer Architecture News

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 1,182

Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning

Yang, T.-J., Chen, Y.-H., & Sze, V. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsGreen IT and Sustainability

Cited by 732

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

Yang, T.-J., Howard, A., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., & Adam, H. (2018) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 572

How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful

Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2020) · IEEE Solid-State Circuits Magazine

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 80

Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators

Yang, T.-J., & Sze, V. (2019) · 2019 IEEE International Electron Devices Meeting (IEDM)

Parallel Computing and Optimization TechniquesAdvanced Memory and Neural Computing

Cited by 55