S. Keckler

Computer Vision and Pattern Recognition · 2000–2019

A researcher in the Science corpus: 16 papers; trusts 245 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 152 citations into it; #16,802 of 21,575 by trust here, #4,781 by citations
• Artificial Intelligence — 109 citations into it
• Electrical and Electronic Engineering — 84 citations into it; #7,362 of 16,539 by trust here, #2,828 by citations
• Efficiency & Compression — 72 citations into it
• Hardware and Architecture — 58 citations into it; #308 of 1,551 by trust here, #195 by citations
• Computer Networks and Communications — 41 citations into it; #3,186 of 9,886 by trust here, #2,687 by citations
• Model Architectures — 38 citations into it
• ML Systems & Hardware — 34 citations into it

Whom S. Keckler trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Kaiming He
6. Ilya Sutskever
7. Yoshua Bengio
8. Christian Szegedy
9. John R. Nickolls
10. Alex Graves

Who trusts S. Keckler overall

1. David A. B. Miller
2. Thomas Moscibroda
3. Ryan Hamerly
4. Dong Ping Zhang
5. Jichuan Chang
6. Derek M. Jones
7. Bradford M. Beckmann
8. Onur Mutlu
9. Kartik Mohanram

Papers from S. Keckler's perspective

Ordered by how strongly the researchers S. Keckler trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SCNN: An accelerator for compressed-sparse convolutional neural networks (2017) — 1,254 citations
• vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design (2016) — 477 citations
• Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks (2017) — 193 citations
• Clock rate versus IPC: the end of the road for conventional microarchitectures (2000) — 736 citations
• Timeloop: A Systematic Approach to DNN Accelerator Evaluation (2019) — 671 citations
• Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture (2019) — 495 citations
• Modeling the effect of technology trends on the soft error rate of combinational logic (2002) — 1,584 citations
• Regional congestion awareness for load balance in networks-on-chip (2008) — 428 citations
• Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications (2017) — 599 citations
• Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs (2019) — 65 citations
• Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design (2016) — 32 citations
• GPUs and the Future of Parallel Computing (2011) — 650 citations
• A NUCA Substrate for Flexible CMP Cache Sharing (2007) — 360 citations
• Research Challenges for On-Chip Interconnection Networks (2007) — 504 citations
• Scaling to the end of silicon with EDGE architectures (2004) — 377 citations
• An adaptive, non-uniform cache structure for wire-delay dominated on-chip caches (2002) — 802 citations