Search Results for author: Sungmin Bae

Found 2 papers, 2 papers with code

A Closer Look at Hardware-Friendly Weight Quantization

1 code implementation • 7 Oct 2022 • Sungmin Bae, Piotr Zielinski, Satrajit Chatterjee

We study the two methods on MobileNetV1 and MobileNetV2 using multiple empirical metrics to identify the sources of performance differences between the two classes, namely, sensitivity to outliers and convergence instability of the quantizer scaling factor.

Quantization

522

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Code

Chip Placement with Deep Reinforcement Learning

2 code implementations • 22 Apr 2020 • Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Sungmin Bae, Azade Nazi, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Anand Babu, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean

To achieve these results, we pose placement as a Reinforcement Learning (RL) problem and train an agent to place the nodes of a chip netlist onto a chip canvas.

reinforcement-learning Reinforcement Learning (RL) +2

Paper
Code

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