Update README

Author: tanner-andrulis

README.md

@@ -1,14 +1,58 @@
-# HWComponents
-The HWComponents (Hardware Components) package, part of the
-[CiMLoop](https://github.com/mit-emze/cimloop) project, provides an interface for the
-estimation of area, energy, latency, and leak power of hardware components in hardware
-architectures. Key features in HWComponents include:
+<div align="center">
 
-[Information about the package is available on the hwcomponents website](https://accelergy-project.github.io/hwcomponents/).
+<img src="https://capsule-render.vercel.app/api?type=rect&color=0:004CFF,100:00CFFF&height=100&section=header&text=HWComponents&fontSize=52&fontColor=ffffff&fontAlignY=55" alt="HWComponents" />
 
-## Citing HWComponents
+***Area, energy, latency, and leak power models for hardware components.***
 
-If you use this package in your work, please cite this repository and the CiMLoop project:
+<br>
+
+[![PyPI](https://img.shields.io/pypi/v/hwcomponents?style=for-the-badge&logo=pypi&logoColor=white&labelColor=3775A9&color=0B4F6C)](https://pypi.org/project/hwcomponents/)
+[![Python](https://img.shields.io/badge/Python-3.12+-3776AB?style=for-the-badge&logo=python&logoColor=white&labelColor=1E415E)](https://www.python.org/)
+[![License](https://img.shields.io/badge/License-MIT-A31F34?style=for-the-badge&labelColor=2D2D2D)](https://opensource.org/licenses/MIT)
+[![Docs](https://img.shields.io/badge/Docs-online-0A7BBB?style=for-the-badge&logo=readthedocs&logoColor=white&labelColor=1A1A1A)](https://accelergy-project.github.io/hwcomponents/)
+
+[![CI](https://img.shields.io/github/actions/workflow/status/Accelergy-Project/hwcomponents/publish.yaml?branch=main&style=for-the-badge&logo=githubactions&logoColor=white&label=CI&labelColor=24292F&color=2EA043)](https://github.com/Accelergy-Project/hwcomponents/actions)
+[![Code style: black](https://img.shields.io/badge/code_style-black-000000?style=for-the-badge&logo=python&logoColor=white&labelColor=2D2D2D)](https://github.com/psf/black)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-2EA043?style=for-the-badge&logo=github&logoColor=white&labelColor=24292F)](https://github.com/Accelergy-Project/hwcomponents/pulls)
+
+</div>
+
+---
+
+HWComponents provides area, energy, latency, and leak power estimates for hardware components in hardware architectures. It is part of the [CiMLoop](https://github.com/mit-emze/cimloop) project and serves as the estimation backend for [AccelForge](https://github.com/Accelergy-Project/accelforge).
+
+Learn more at the [website](https://accelergy-project.github.io/hwcomponents/) or on [GitHub](https://github.com/Accelergy-Project/hwcomponents).
+
+## ⚡ Features
+
+- **Simple Python API** for writing area, energy, latency, and leak power models. New models can be written in minutes.
+- **Automatic parameter scaling** across configurations, including scaling to different technology nodes.
+- **Plugin ecosystem** that automatically gathers components from installed Python packages.
+- **Included model packages** for multiple projects, plus a general-purpose component library.
+
+## 📦 Install
+
+```bash
+# Core package
+pip install hwcomponents
+
+# Model packages
+pip install hwcomponents-cacti
+pip install hwcomponents-neurosim
+pip install hwcomponents-adc
+pip install hwcomponents-library
+
+# List available models
+hwc --list
+```
+
+## 🧪 Examples
+
+See the [`notebooks/`](notebooks) directory for tutorials, and the [docs](https://accelergy-project.github.io/hwcomponents/) for the full API.
+
+## 📚 Cite
+
+If you use HWComponents in your work, please cite this repository and the CiMLoop project:
 
 ```bibtex
 @software{hwcomponents,
@@ -23,10 +67,7 @@ If you use this package in your work, please cite this repository and the CiMLoo
   booktitle={2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
   title={CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool},
   year={2024},
-  volume={},
-  number={},
   pages={10-23},
-  keywords={Performance evaluation;Accuracy;Computational modeling;Computer architecture;Artificial neural networks;In-memory computing;Data models;Compute-In-Memory;Processing-In-Memory;Analog;Deep Neural Networks;Systems;Hardware;Modeling;Open-Source},
   doi={10.1109/ISPASS61541.2024.00012}
 }
 ```