harvard-acc/smaug
SMAUG: Simulating Machine Learning Applications Using Gem5-Aladdin
SMAUG helps deep learning researchers simulate and evaluate how well different hardware accelerators and System-on-Chip (SoC) designs perform with deep learning models. It takes your deep learning model and custom hardware designs as input, then provides detailed performance simulations. This allows researchers to compare and optimize accelerator and SoC configurations for their specific deep learning applications.
114 stars. No commits in the last 6 months.
Use this if you are a deep learning researcher needing to evaluate the performance of custom hardware accelerators or SoC designs for deep learning models.
Not ideal if you are looking for a tool to train deep learning models or deploy them on existing hardware.
Stars
114
Forks
29
Language
C++
License
BSD-3-Clause
Category
Last pushed
Jan 04, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/harvard-acc/smaug"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pytorch/executorch
On-device AI across mobile, embedded and edge for PyTorch
catalyst-team/catalyst
Accelerated deep learning R&D
z-mahmud22/Dlib_Windows_Python3.x
Dlib compiled binaries (.whl) for Python 3.7-3.14 and Windows x64
mit-han-lab/mcunet
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2:...
gigwegbe/tinyml-papers-and-projects
This is a list of interesting papers and projects about TinyML.