Nam Sung Kim

Hardware and Architecture · 2002–2021

A researcher in the Science corpus: 11 papers; trusts 61 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Hardware and Architecture — 80 citations into it
• Computer Vision and Pattern Recognition — 73 citations into it
• Artificial Intelligence — 67 citations into it
• Electrical and Electronic Engineering — 65 citations into it
• Computer Networks and Communications — 55 citations into it
• ML Systems & Hardware — 38 citations into it
• Optimization & Training — 35 citations into it
• Information Systems — 20 citations into it

Whom Nam Sung Kim trusts overall

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

1. Alex Krizhevsky
2. Anantha P. Chandrakasan
3. Kaiming He
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. T. Mudge
8. Ilya Sutskever
9. Martin A. Zinkevich
10. Mu Li

Who trusts Nam Sung Kim overall

1. Adrian Sampson
2. Kaushik Roy

Papers from Nam Sung Kim's perspective

Ordered by how strongly the researchers Nam Sung Kim 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. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Learning Multiple Layers of Features from Tiny Images
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Going deeper with convolutions
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Drowsy caches: simple techniques for reducing leakage power (2002) — 922 citations
• Razor: a low-power pipeline based on circuit-level timing speculation (2003) — 1,460 citations
• NDA: Near-DRAM acceleration architecture leveraging commodity DRAM devices and standard memory modules (2015) — 295 citations
• Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks (2020) — 143 citations
• Leakage Current: Moore's Law Meets Static Power (2003) — 1,329 citations
• Approximate Computing: A Survey (2016) — 497 citations
• Energy-Efficient and Metastability-Immune Resilient Circuits for Dynamic Variation Tolerance (2009) — 359 citations
• Hardware Architecture and Software Stack for PIM Based on Commercial DRAM Technology : Industrial Product (2021) — 329 citations
• GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training (2018) — 70 citations
• Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training (2018) — 112 citations
• GPUWattch: enabling energy optimizations in GPGPUs (2013) — 611 citations