Difference between revisions of "Design and Implementation of a Convolutional Neural Network Accelerator ASIC"
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: Explore various architectures to perform the 2D convolutions used in convolutional networks, considering the constraints of an ASIC design project. | : Explore various architectures to perform the 2D convolutions used in convolutional networks, considering the constraints of an ASIC design project. | ||
: Get to know the ASIC design flow from specification through architecture exploration to implementation, functional verification, back-end design and silicon testing. | : Get to know the ASIC design flow from specification through architecture exploration to implementation, functional verification, back-end design and silicon testing. | ||
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+ | ===Literature=== | ||
===Practical Details=== | ===Practical Details=== |
Revision as of 19:16, 6 December 2014
Contents
Short Description
todo
Status: In Progress
- David Gschwend, Christoph Mayer, Samuel Willi
- Supervision: Lukas Cavigelli, Beat Muheim
- Date: Fall Semester 2014 (sem14h17, sem14h18, sem14h19)
Prerequisites
- Knowledge of Matlab
- Interest in video processing and VLSI design
- VLSI 1 and enrolment in VLSI 2 is required
- At least one student has to test the chip as part of the VLSI 3 lecture, if the ASIC should be manufactured.
Character
- 10% Theory / Literature Research
- 60% VLSI Architecture, Implementation & Verification
- 30% VLSI back-end Design
Professor
Detailed Task Description
Goals
- Explore various architectures to perform the 2D convolutions used in convolutional networks, considering the constraints of an ASIC design project.
- Get to know the ASIC design flow from specification through architecture exploration to implementation, functional verification, back-end design and silicon testing.
Literature
Practical Details
Results
Links
- The EDA wiki with lots of information on the ETHZ ASIC design flow (internal only) [[1]]