Course map
- 1 lesson
Starts with How to Use This Course
- 1 lesson
Starts with AI-VLSI Metrics Reference
- 1 lesson
Starts with AI-VLSI Glossary
- 1 lesson
Starts with Reading Path
- 61 lessons
Starts with Linear Algebra & Tensors: Mechanism
- 61 lessons
Starts with Perceptron to MLP: Mechanism
- 61 lessons
Starts with Datasets, Splits & Augmentation: Mechanism
- 61 lessons
Starts with CPU, GPU, TPU, NPU Landscape: Mechanism
- 61 lessons
Starts with MAC Arrays & Systolic: Mechanism
- 61 lessons
Starts with ML for EDA Placement & Routing: Mechanism
- 61 lessons
Starts with ONNX & Runtime Engines: Mechanism
- 4 lessons
Starts with AI-VLSI Whiteboard Framework
- 1 lesson
Starts with AI Basics for VLSI Engineers Cheatsheet
About this course
Course promise
This is the practical AI literacy course for VLSI teams. It teaches enough ML depth to reason about models, hardware implications, EDA trends, and deployment tradeoffs without requiring ML research specialization.
Translate ML terms into design, signoff, and silicon decisions.
Understand compute/dataflow/precision implications for hardware teams.
Evaluate EDA ML proposals with measurable risk and ownership.
Use a repeatable framework: metric -> mechanism -> artifact -> owner -> decision.
Course map
Show how the course is organised
ML fundamentals + neural mechanics
-> data pipeline and training discipline
-> compute hardware and roofline constraints
-> accelerator architecture and bandwidth closure
-> EDA ML workflow integration
-> edge deployment + MLOps governanceRelated topics
Section deep dive
AI for VLSI success needs data discipline, architecture context, and operational governance.
Concept diagram
Show Concept diagram
problem -> model -> hardware mapping -> deploymentMetric graph
Show Metric graph
quality vs efficiency trendReports and artifacts
model quality report
runtime profile
deployment readiness memo
Mini case study
Freeze revisions before root-cause debate.
Debug branches
Isolate first failing layer
Senior review question
Ask: what evidence connects this ML claim to a concrete VLSI workflow decision and owner signoff?
Execution drill pack 1
Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi: metric framing, mechanism proof, hardware implications, and release safety.
Evidence checklist
Metric context includes workload, dataset slice, and revision tags.
Mechanism explanation links model behavior to observed outcome.
Hardware/runtime feasibility is profiled, not assumed.
Owner and rollback path are documented before rollout.
Review prompts
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
Evidence capsule
Show Evidence capsule
AI-VLSI EVIDENCE CAPSULE 1
PATH: ai-vlsi
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>Execution drill pack 2
Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi: metric framing, mechanism proof, hardware implications, and release safety.
Evidence checklist
Metric context includes workload, dataset slice, and revision tags.
Mechanism explanation links model behavior to observed outcome.
Hardware/runtime feasibility is profiled, not assumed.
Owner and rollback path are documented before rollout.
Review prompts
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
Evidence capsule
Show Evidence capsule
AI-VLSI EVIDENCE CAPSULE 2
PATH: ai-vlsi
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>Execution drill pack 3
Use this pack to rehearse AI-for-VLSI decision making on ai-vlsi: metric framing, mechanism proof, hardware implications, and release safety.
Evidence checklist
Metric context includes workload, dataset slice, and revision tags.
Mechanism explanation links model behavior to observed outcome.
Hardware/runtime feasibility is profiled, not assumed.
Owner and rollback path are documented before rollout.
Review prompts
Which decision will this model output influence?
What is the first failing layer when metric regresses?
Which owner applies the smallest reversible fix?
What validation matrix is required before deployment?
Evidence capsule
Show Evidence capsule
AI-VLSI EVIDENCE CAPSULE 3
PATH: ai-vlsi
WORKLOAD SLICE: <name>
PRIMARY METRIC: <value/trend>
FIRST FAILING LAYER: <data/model/runtime/hardware>
OWNER: <name>
PRIMARY ARTIFACT: <report/profile/dashboard>
DECISION: <ship / rollback / escalate>