Networks Design Projects

COMPSCI 2440R Subject & Catalog Number

Description

The contents and course requirements of CS 244R are similar to those of CS 1440R, with the exception that students enrolled in CS 2440R are expected to do substantial system implementation and conduct rigorous performance analysis for their design projects, and perform graduate-level work to earn the same letter grade. In this course, students will learn how to utilize tools powered by large language models (LLMs) to address real-world design challenges, including the generation of code and circuits.

This course examines how large language models (LLMs) can be applied to real-world design problems, including the generation of software and hardware (circuit) designs. Reasoning models now produce designs that are more often both correct and efficient. However, test-Time scaling raises inference cost, and generated designs must still be verified, for example with automatically generated testbenches.

Lectures cover reasoning LLMs, AI compute infrastructure, post-training (SFT, RLVR, GRPO), efficient inference (speculative decoding, quantization, KV-cache management), and agentic and multi-agent systems.

Students will:
- Complete assigned readings before each lecture.
- Study 12 research papers, present one in class, and review the other 11 (strengths, weaknesses, future directions).
- Take a midterm on the lectures and papers.
- Complete a team project (2–3 students) using LLM-assisted tools that contributes  novel ideas, supported by an implementation and performance evaluation.

Course Notes

Preference will be given to upper-class undergraduates or graduate students majoring in Computer Science or Electrical Engineering. Please note that this course was previously listed as CS 244R.

Recommended Prep

Courses in Artificial Intelligence (AI), such as CS 1810 or CS 1820, as well as courses in AI computing, like CS 2420, and additionally, programming experience in languages such as Python or C++ and some research experience.