cs217.stanford.edu
Open in
urlscan Pro
2606:50c0:8001::153
Public Scan
URL:
https://cs217.stanford.edu/
Submission Tags: phishingrod
Submission: On October 28 via api from DE — Scanned from DE
Submission Tags: phishingrod
Submission: On October 28 via api from DE — Scanned from DE
Form analysis 0 forms found in the DOM
Text Content
HARDWARE ACCELERATORS FOR MACHINE LEARNING (CS 217) STANFORD UNIVERSITY, WINTER 2023 Bespoke and Customized This course provides in-depth coverage of the architectural techniques used to design accelerators for training and inference in machine learning systems. This course will cover classical ML algorithms such as linear regression and support vector machines as well as DNN models such as convolutional neural nets, and recurrent neural nets. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. We will cover the design of accelerators for ML model inference and training. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. To design energy-efficient accelerators, students will develop the intuition to make trade-offs between ML model parameters and hardware implementation techniques. Students will read recent research papers and complete a design project. INSTRUCTORS AND OFFICE HOURS: Ardavan Pedram Office Hours TBA Kunle Olukotun Office Hours TBA This class meets Tuesday and Thursday from 10:30 - 11:50 AM in Gates B03. TEACHING ASSISTANTS Nathan Zhang Office Hours Tuesday 12-1 PM Gates 498+Zoom, Friday 3-4 PM Zoom CLASS INFORMATION Funding for this research/activity was partially provided by the National Science Foundation Division of Computing and Communication Foundations under award number 1563113. SCHEDULE GUEST LECTURES David Kanter, MLCommons MLPerf Thursday February 9, 2023 -------------------------------------------------------------------------------- Raghu Prabhakar, Sambanova Reconfigurable Dataflow Architectures Tuesday February 14, 2023 -------------------------------------------------------------------------------- Jared Casper, Nvidia Large Language Models Thursday February 16, 2023 -------------------------------------------------------------------------------- Dan Fu, Stanford Flash Attention Tuesday February 21, 2023 -------------------------------------------------------------------------------- Greg Diamos, Something New Data systems for large models Thursday February 23, 2023 -------------------------------------------------------------------------------- Swapnil Gandhi, Stanford Graph Neural Networks Tuesday February 28, 2023 -------------------------------------------------------------------------------- Sameer Kumar, Google Distributed Systems Thursday March 2, 2023 -------------------------------------------------------------------------------- Mike Houston, NVIDIA Distributed Systems for Deep Learning Tuesday March 7, 2023 -------------------------------------------------------------------------------- Ce Zhang, ETH Thursday March 9, 2023 -------------------------------------------------------------------------------- Cliff Young, Google Hierarchical Codesign and TPU4 Thursday March 16, 2023 LECTURE NOTES (FALL 2018) RELATED STANFORD COURSES * CS230 * CS231n * STATS 385 READING LIST AND OTHER RESOURCES BASIC INFORMATION ABOUT DEEP LEARNING CHEAT SHEET – THINGS THAT EVERYONE NEEDS TO KNOW BLOGS GRADING This page was generated by GitHub Pages.