News

The IR-drop (voltage drop) analysis in integrated circuits is essential as the circuit designs become more complex and compact. Due to the complicated and tiny designs, the power delivery network (PDN) cannot deliver a target voltage to each cell, causing reliability issues and performance drops. Therefore, IR-drop estimation is a crucial step to ensure the functionality of the circuits.

Traditionally, IR-drop estimation relies on solving linear equations based on Kirchhoff’s current and voltage laws. Nevertheless, as the designs become more complex, the computational cost and simulation time increase significantly. In this research, an energy-efficient and high-performance static IR-drop estimation called GIRD (Green IR-Drop) is proposed.  GIRD processes the IC design input in three steps. First, the input netlist data are converted to multi-channel maps. Their joint spatial-spectral representations are determined with PixelHop. Next, discriminant features are selected using the relevant feature test (RFT). Finally, the selected features are fed to the XGBoost (eXtreme Gradient Boosting trees) regressor. Both PixelHop and RFT are green learning tools. GIRD yields a low carbon footprint due to its smaller model sizes and lower computational complexity. Besides, its performance scales well with small training datasets. Experiments on synthetic and real circuits are given to demonstrate the superior performance of GIRD. The model size and the complexity, measured by the Floating Point Operations (FLOPs) of GIRD, are only 1/1000 and 1/100 of deep-learning methods, respectively.

We are so happy to welcome a new MCL member, Cynthia Huang joining MCL this semester. Here is a quick interview with Cynthia:

1. Could you briefly introduce yourself and your research interests?

I am Cynthia Huang, a junior undergraduate student majoring in Computer Engineering and Computer Science at USC. My research interests include machine learning, AutoML, and computer vision. Previously, I have worked on AutoML, specifically on simultaneous optimization of neural network architecture and weights. I hope to continue deepening my exploration of these fields in the future.

2. What is your impression about MCL and USC?

My impression of MCL is that it is a collaborative and inspiring group, where many talented individuals come together with exciting ideas. I truly appreciate the team spirit and supportive environment, where everyone is open to sharing insights and learning from one another. I find great joy in engaging in research discussions, exchanging perspectives, and brainstorming solutions to challenging problems in MCL. I am very excited about the opportunity to collaborate with everyone and look forward to contributing to meaningful research in the future!

3. What are your future expectations and plans in MCL?

Currently, I am working on semantic segmentation using green learning. In the future, I look forward to continuing research discussions and collaborations with everyone at MCL. I hope to further explore new ideas, exchange knowledge, and contribute to exciting research. Additionally, I am excited to connect with more members of the MCL community.

Welcome to the Spring 2025 semester! 🌸 We hope everyone had a refreshing break and is ready to dive into an exciting new chapter of research, learning, and collaboration.

As always, this semester holds endless possibilities for innovation and discovery. Let’s continue to support each other, exchange ideas, and push the boundaries of knowledge. Remember, every great breakthrough starts with curiosity and hard work!

Wishing you all the best of luck in your projects, papers, and presentations. Let’s make this semester one of great achievements for the MCL Lab and the USC community!

As 2024 draws to a close, we reflect on a year of growth, success, and countless blessings at MCL. This year, we bid a fond farewell to our esteemed graduates, whose impactful research leaves an enduring legacy as they step into exciting new chapters. At the same time, we joyfully welcomed fresh faces into our MCL family—brimming with energy and enthusiasm for groundbreaking research.

Through dedication, collaboration, and resilience, our members have achieved remarkable milestones, including publishing exceptional work in prestigious journals and conferences. These accomplishments are a testament to the passion and hard work of every individual in our community.

Now, as the holiday season unfolds with its sparkle of joy and the warmth of togetherness, we take a moment to celebrate the achievements of this past year and the unity that binds us as the MCL family.

From all of us at MCL, we wish you a Merry Christmas filled with cheer, love, and laughter. May this festive season bring you peace, joy, and countless reasons to smile!

Image segmentation is a computer vision process involving dividing an image into segments based on shared characteristics such as colour, texture, or intensity. Semantic segmentation, specifically, is a type of image segmentation assigning a class label to each pixel in an image. This approach is widely used in autonomous driving, object detection and medical imaging.

To address such problems, we have proposed a green image segmentation approach that identifies each image segment without requiring backpropagation. The image is resized and divided into non-overlapping patches, with each patch labelled as pure or impure based on its class composition. Impure patches are iteratively enlarged and subdivided until all patches are labelled by class. This is a promising method, but it still requires further refinement to deal with incorrectly classified large patches and object boundaries.