CSCI-GA.3033/ECE-GY9483

Efficient AI Computing: Algorithm

and Implementation

fALL 2026

The course will focus on recent advancements in the design of efficient neural networks, specifically on how to create and optimize AI models for improved performance, scalability, and resource efficiency. Students will explore key techniques like model compression, pruning, quantization, and model distillation for CNN, RNN, Transformer and LLM, aimed at reducing computational complexity and memory usage while maintaining accuracy. Additionally, the course will cover efficient training and inference methods, including distributed computing, parallelism, and low-precision computation, which are crucial for deploying AI on resource-limited devices such as smartphones or edge computing systems. Students will also study advanced hardware architectures of AI system, AI compiler and hardware accelerators, gaining insights into the hardware implementation of neural network computations on these specialized systems.

 

Lecture Time: Wednesday 7:10-9:10pm EST (Zoom)

Lecture Location: 31 Washington Pl (Silver Ctr), Room 401

Readings: Course slides and papers

Suggested readings: Goodfellow, Ian. "Deep learning." (2016). https://www.deeplearningbook.org/

Evaluation Breakdown:

• Assignments (30%): total three of them, each counts 10%

• In-course quiz (10%) 

• Midterm (30%) 

• Final project (30%)

  • Proposal (1 page) 5%

  • Final presentation 15%

  • Final report 10%

Late Submission Policy: 

• A 10% deduction will be applied to the original grade if submitted within 24 hours; otherwise, a 30% deduction will be applied.

Syllabus 

Contact:

• For questions related to course materials, use efficientaiaccelerator@gmail.com 

  • When you send your email, please start the email title with three categories to indicate the type of the question: [Algorithm], [System], [Others].

• For other inquiries, personal matters, or emergencies, you can email me at sai.zhang@nyu.edu

Course Schedule