| Name | zigzag-dse JSON |
| Version |
3.8.1
JSON |
| download |
| home_page | None |
| Summary | ZigZag - Deep Learning Hardware Design Space Exploration |
| upload_time | 2024-12-24 09:20:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License Copyright (c) 2021 MICAS (KU Leuven) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
zigzag
dse
design-space-exploration
machine-learning
deep-learning
mapping
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
networkx
sympy
matplotlib
onnx
tqdm
multiprocessing_on_dill
pyyaml
pytest
typeguard
cerberus
seaborn
pre-commit
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 🌀 ZigZag
[](https://github.com/pylint-dev/pylint)
**ZigZag** is a novel HW Architecture-Mapping Design Space Exploration (DSE) framework for Deep Learning (DL) accelerators. It bridges the gap between algorithmic DL decisions and their acceleration cost on specialized hardware, providing **fast and accurate HW cost estimation**. Through its advanced mapping engines, ZigZag automates the discovery of optimal mappings for complex DL computations on custom architectures.
### 🌟 [**Explore Documentation**](https://kuleuven-micas.github.io/zigzag/)
### 📖 [**Start Tutorial**](https://github.com/KULeuven-MICAS/zigzag/tree/tutorial)
---
## ✨ Key Features
✔ **ONNX Integration**: Directly parse ONNX models for seamless compatibility with modern deep learning workflows.
✔ **Flexible Hardware Architecture**: Supports multi-dimensional (>2D) MAC arrays, advanced interconnection patterns, and high-level memory structures.
✔ **Enhanced Cost Models**: Includes detailed energy and latency analysis for memories with variable port structures through inferred spatial and temporal data sharing and reuse patterns.
✔ **Modular and Extensible**: Fully revamped structure with object-oriented paradigms to support user-friendly extensions and interfaces.
✔ **Integrated In-Memory Computing Support**: Seamlessly define digital and analog in-memory-computing (IMC) cores via an intuitive user interface.
✔ **Comprehensive Output Options**: Outputs results in YAML format, enabling further analysis and integration.
---
## 🚀 Installation
Visit the [Installation Guide](https://kuleuven-micas.github.io/zigzag/installation.html) for step-by-step instructions to set up ZigZag on your system.
---
## 📖 Getting Started
Get up to speed with ZigZag using our resources:
- Check out the [Getting Started Guide](https://kuleuven-micas.github.io/zigzag/getting-started.html).
- Explore the [Jupyter Notebook Demo](https://github.com/ZigZag-Project/zigzag-demo) to see ZigZag in action.
---
## 🔧 What’s Next
We are continuously improving ZigZag to stay at the forefront of HW design space exploration. Here’s what we’re working on:
- 🧠 **ONNX Operator Support**: Expanding compatibility for modern generative AI workloads.
- 📂 **Novel Memory Models**: Integrating advanced memory models and compilers for better performance analysis.
- ⚙️ **Automatic Hardware Generation**: Enabling end-to-end generation of hardware configurations.
- 🚀 **Enhanced Mapping Methods**: Developing more efficient and intelligent mapping techniques.
#### ⭐ Please consider starring this repository to stay up to date!
---
## 📚 Publication Pointers
Learn more about the concepts behind ZigZag and its applications:
### The General Idea of ZigZag
- **[ZigZag: Enlarging Joint Architecture-Mapping Design Space Exploration for DNN Accelerators](https://ieeexplore.ieee.org/document/9360462)**
L. Mei, P. Houshmand, V. Jain, S. Giraldo, M. Verhelst
_IEEE Transactions on Computers_, vol. 70, no. 8, pp. 1160-1174, Aug. 2021.
### Advanced Features and Extensions
- **[Uniform Latency Model for DNN Accelerators](https://lirias.kuleuven.be/retrieve/661303)**
L. Mei, H. Liu, T. Wu, et al.
_DATE 2022_.
- **[LOMA: Fast Auto-Scheduling on DNN Accelerators](https://ieeexplore.ieee.org/document/9458493)**
A. Symons, L. Mei, M. Verhelst
_AICAS 2021_.
For more publications and detailed case studies, refer to the full list in our [Documentation](https://kuleuven-micas.github.io/zigzag/).
---
## 💻 Contributing
We welcome contributions! Feel free to fork the repository, submit pull requests, or open issues. Check our [Contributing Guidelines](CONTRIBUTING.md) for more details.
---
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