Course Schedule
Being less concrete further out, the course scheduling is tentative and subject to changes.
Introduction, AI systems foundation
Week 1
- 08/24
- Fill out background survey by 08/25
- 08/26
Reading: The Bitter Lesson (required); Hidden Technical Debt in Machine Learning Systems (optional); Software 2.0 (optional)
- 08/28
- Inference Speedrun out
Week 2
- 08/31
Reading: Floating Point and IEEE 754 (optional); Using FP8 and FP4 with Transformer Engine (optional); Defeating Nondeterminism in LLM Inference (optional)
- 09/02
Reading: mlsysbook: Quantization and Precision (required); Building a quantization paradigm from first principles (optional)
Week 3
- 09/07
Reading: ZipLLM [NSDI’26] (optional); TensorDex [SOSP’26] (optional)
Pickle safety: Demo
- 09/09
AI infrastructure
Week 4
- 09/14
Reading: mlsysbook: Hardware Acceleration (required); Memory wall UVA tech report (optional); Roofline model UC Berkeley tech report (optional); OpenAI Jalapeño first results (optional)
- 09/16
Reading see 09/14
Systolic array: Demo
Inference Speedrun artifact due (09/16)
- 09/17
Project team signup due (09/17)
Week 5
Model training paradigm and performance engineering
Week 6
- 09/28
- 09/30
Project proposal due (09/30)
LLM inference
Week 7
- 10/05
Reading day (no class)
- 10/07
Paper presentation signup due (10/07)
Week 8
Student paper presentations, guest lectures
Week 9
- 10/19
TBD
- 10/21
TBD
Week 10
- 10/26
TBD
- 10/28
Project checkpoint presentation
Week 11
- 11/02
TBD
- 11/04
TBD
Week 12
- 11/09
TBD
- 11/11
TBD
Week 13
- 11/16
TBD
- 11/18
TBD
Inference Speedrun report due
Week 14
- 11/23
TBD
- 11/25
Thanksgiving recess (no class)
Week 15
- 11/30
Project presentation I
- 12/02
Project presentation II
Week 16
- 12/07
Project presentation III
- 12/09
Project everything due