Luis Ceze — Accelerating Machine Learning Systems

From Apache TVM to OctoML, Luis gives direct insight into the world of ML hardware optimization, and where systems optimization is heading.

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Luis Ceze is co-founder and CEO of OctoML, co-author of the Apache TVM Project, and Professor of Computer Science and Engineering at the University of Washington. His research focuses on the intersection of computer architecture, programming languages, machine learning, and molecular biology.

Connect with Luis:

📍 Twitter: https://twitter.com/luisceze

📍 University of Washington profile: https://homes.cs.washington.edu/~luisceze/

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⏳ Timestamps:

0:00 Intro and sneak peek

0:59 What is TVM?

8:57 Freedom of choice in software and hardware stacks

15:53 How new libraries can improve system performance

20:10 Trade-offs between efficiency and complexity

24:35 Specialized instructions

26:34 The future of hardware design and research

30:03 Where does architecture and research go from here?

30:56 The environmental impact of efficiency

32:49 Optimizing and trade-offs

37:54 What is OctoML and the Octomizer?

42:31 Automating systems design with and for ML

44:18 ML and molecular biology

46:09 The challenges of deployment and post-deployment

🌟 Transcript: http://wandb.me/gd-luis-ceze 🌟

Links:

1. OctoML: https://octoml.ai/

2. Apache TVM: https://tvm.apache.org/

3. "Scalable and Intelligent Learning Systems" (Chen, 2019): https://digital.lib.washington.edu/researchworks/handle/1773/44766

4. "Principled Optimization Of Dynamic Neural Networks" (Roesch, 2020): https://digital.lib.washington.edu/researchworks/handle/1773/46765

5. "Cross-Stack Co-Design for Efficient and Adaptable Hardware Acceleration" (Moreau, 2018): https://digital.lib.washington.edu/researchworks/handle/1773/43349

6. "TVM: An Automated End-to-End Optimizing Compiler for Deep Learning" (Chen et al., 2018): https://www.usenix.org/system/files/osdi18-chen.pdf

7. Porcupine is a molecular tagging system introduced in "Rapid and robust assembly and decoding of molecular tags with DNA-based nanopore signatures" (Doroschak et al., 2020): https://www.nature.com/articles/s41467-020-19151-8

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