Kim M. Hazelwood

Artificial Intelligence · 2005–2021

A researcher in the Science corpus: 15 papers; trusts 23 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 197 citations into it; #27,230 of 35,345 by trust here, #5,720 by citations
• Computer Vision and Pattern Recognition — 190 citations into it; #20,384 of 21,575 by trust here, #6,426 by citations
• Computer Networks and Communications — 86 citations into it; #3,809 of 9,886 by trust here, #461 by citations
• Information Systems — 80 citations into it; #5,067 of 8,771 by trust here, #686 by citations
• Hardware and Architecture — 77 citations into it; #655 of 1,551 by trust here, #123 by citations
• ML Systems & Hardware — 57 citations into it; #520 of 575 by trust here, #202 by citations

Whom Kim M. Hazelwood trusts overall

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

1. Luiz André Barroso
2. Nathan Bell
3. Joseph Dean
4. Kaiming He
5. Priya Goyal
6. Yoshua Bengio
7. Yann LeCun
8. Ross B. Girshick
9. Geoffrey E. Hinton
10. Michael Garland

Who trusts Kim M. Hazelwood overall

1. Udit Gupta
2. Carole-Jean Wu
3. Akshitha Sriraman
4. Bilge Acun
5. Mengjia Yan
6. Stefanos Laskaridis
7. Abhishek Dhanotia
8. Young Geun Kim
9. Haoran Qiu
10. Xiaodong Wang

Papers from Kim M. Hazelwood's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Going deeper with convolutions
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• MLPerf Training Benchmark (2019) — 388 citations
• Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective (2018) — 626 citations
• Sustainable AI: Environmental Implications, Challenges and Opportunities (2021) — 662 citations
• Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads (2021) — 22 citations
• Understanding Training Efficiency of Deep Learning Recommendation Models at Scale (2020) — 128 citations
• SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019) — 273 citations
• Machine Learning at Facebook: Understanding Inference at the Edge (2019) — 490 citations
• MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
• The Architectural Implications of Facebook's DNN-Based Personalized Recommendation (2019) — 330 citations
• Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (2018) — 205 citations
• Profiling a warehouse-scale computer (2015) — 513 citations
• Tradeoffs between power management and tail latency in warehouse-scale applications (2014) — 70 citations
• Where is the data? Why you cannot debate CPU vs. GPU performance without the answer (2011) — 320 citations
• Pin: building customized program analysis tools with dynamic instrumentation (2005) — 4,533 citations