Next class · Week 01 ·
Course Introduction: AI as Workload, AI as Designer
Required reading
COMS 6998 · Columbia University · Fall 2026
AI-Native Computing
Hardware for AI AI for Hardware
AI is transforming computing in two directions: emerging AI workloads demand new hardware and system architectures, while AI is becoming a powerful tool for designing computing systems themselves.
Module 1: Computing for AI
Profile, serve, schedule, map, accelerate, and make reliable emerging LLM, agentic, physical, and compositional AI workloads.
Module 2: AI for Computing
Use agents to design, optimize, and verify software, compilers, architectures, SoCs, RTL, EDA flows, and chips.
Shared methodology. Dynamic workflows, closed-loop feedback, cross-layer optimization, heterogeneous resources, quality-performance-cost tradeoffs, and evidence-driven evaluation.
What you'll learn
Profile and diagnose AI systems. Represent an AI application as a pipeline, dynamic DAG, or feedback loop; measure latency, throughput, utilization, energy, and cost; locate bottlenecks with roofline reasoning, queueing, and trace analysis.
Reason across the stack. Connect model, software, runtime, architecture, memory, accelerator, SoC, and deployment decisions, and evaluate joint quality-performance-energy-cost tradeoffs.
Serve and accelerate emerging workloads. LLM and agent serving, embodied and physical AI inference, neuro-symbolic acceleration, datacenter accelerators, and SoCs.
Build AI that designs computing systems. Formulate system design as an agent environment with state, actions, tools, and feedback; compare LLM agents, RL, Bayesian optimization, and classical heuristics under matched budgets.
Audit claims like a reviewer. Read papers and industry claims against baselines, budgets, ablations, and held-out evidence.
Produce conference-style research. A semester-long project with meaningful baselines, ablations, failure analysis, and a reproducible artifact.
Prerequisites & expectations
- Expected
- Basic computer organization or systems knowledge, familiarity with machine-learning concepts, and the ability to program and run quantitative experiments.
- Helpful, not required
- Experience with CUDA, compilers, digital design, RTL, EDA, robotics simulators, FPGA platforms, LLM agents, or research-paper reading. No one is expected to arrive with expertise across the entire stack.
- Project readiness
- Each team should bring enough complementary expertise to implement, measure, and evaluate its selected project.
Scope note. The course covers cross-layer computing systems, spanning computer architecture, software systems, and silicon, for emerging AI workloads such as physical, embodied, neuro-symbolic, and agentic AI; and agentic AI methods that design, optimize, and verify computing systems themselves. The two directions close a loop: better computing enables stronger AI, and stronger AI builds better computing.
Now
Start here this week
Next class · Week 02 ·
How to Study an AI Computing System + LLM Inference Fundamentals
Required reading
Next class · Week 03 ·
Serving Systems for LLMs and AI Agents
Required reading
Next class · Week 04 ·
AI Hardware: Accelerators and System-on-Chips
Required reading
Next class · Week 05 ·
Physical AI I: Inference Systems and Serving for Embodied AI
Required reading
Next class · Week 06 ·
Physical AI II: Hardware-Software Co-Design and Architecture
Required reading
- DaDu-Corki: Algorithm-Architecture Co-Design for Embodied AI-Powered Robotic Manipulation ISCA 2025
- ReCA: Integrated Acceleration for Real-Time and Efficient Cooperative Embodied Autonomous Agents ASPLOS 2025
- Deltoris: Enabling Real-Time VLA Inference in Embodied AI via Bit-Level Sparsity and Speculative Inference MICRO 2026
Next class · Week 07 ·
Accelerating Neuro-Symbolic and Compositional AI
Required reading
Next class · Week 08 ·
AI for Software Systems: Compilers and GPU Kernels
Required reading
Next class · Week 09 ·
Midterm Project Presentations
Required reading
No assigned readings.
Next class · Week 10 ·
AI for Computer Architecture I: Measuring and Exploring
Required reading
Next class · Week 11 ·
AI for Computer Architecture II: Agentic Design Systems
Required reading
Next class · Week 12 ·
AI for RTL and Chip Physical Design
Required reading
- A Graph Placement Methodology for Fast Chip Design (AlphaChip) Nature 2021 (+2024 addendum)
- ChipNeMo: Domain-Adapted LLMs for Chip Design NVIDIA 2024
- Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification arXiv 2025
Next class · Week 13 ·
Final Project Poster Session
Required reading
No assigned readings.
Next deadline
background survey
paper preference form
project bidding form
P0 project proposal & team charter
P1 infrastructure & baselines
P2 prototype & pilot results
midterm slides
P3 evaluation plan & initial results
P4 main results & ablations
posters in shared folder
P5 complete draft & artifact
final paper & artifact
No upcoming deadlines.
Announcements
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Course website is live
The complete schedule and reading list are now available. Material updates will also be announced in Canvas.
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First meeting
We meet Friday, September 11, 10:10 AM-12:00 PM in 602 Northwest Corner.