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About Hardware Accelerators For Machine Learning (CS 217) by cs217
This document provides information about a course titled "Hardware Accelerators for Machine Learning (CS 217)" offered at Stanford University in Winter 2020. The course covers architectural techniques for designing accelerators for machine learning training and inference. It will discuss algorithms like linear regression, support vector machines, and deep neural networks. Students will learn how to design accelerators that use techniques like parallelism, locality, and low precision to implement core ML computations efficiently. The course involves reading papers and completing a design project. It meets on Tuesdays and Thursdays from 10:30-11:50 AM and is taught by professors Ardavan Pedram and Kunle Olukotun with teaching assistant Nathan Zhang.
Detailed Information
| Author: | ['Pramod Reddy R'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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