Add TMVA SOFIE Project Proposal for GSoC 2025

_gsocproposals/2025/proposal_TMVA-SOFIE-GPU.md

@@ -0,0 +1,42 @@
+---
+title: TMVA SOFIE - GPU Support for Machine Learning Inference
+layout: gsoc_proposal
+project: ROOT
+year: 2025
+organization: CERN
+difficulty: medium
+duration: 350
+mentor_avail: Flexible
+---
+
+# Description
+SOFIE (System for Optimized Fast Inference code Emit) is a Machine Learning Inference Engine within TMVA (Toolkit for Multivariate Data Analysis) in ROOT. SOFIE offers a parser capable of converting ML models trained in Keras, PyTorch, or ONNX format into its own Intermediate Representation, and generates C++ functions that can be easily invoked for fast inference of trained neural networks. Using the IR, SOFIE can produce C++ header files that can be seamlessly included and used in a 'plug-and-go' style.
+
+SOFIE currently supports various Machine Learning operators defined by the ONNX standards, as well as a Graph Neural Network (GNN) implementation. It supports the parsing and inference of Graph Neural Networks trained using DeepMind Graph Nets.
+
+As SOFIE continues to evolve, there's a need to enable inference on GPUs. This project aims to explore different GPU stacks (such as CUDA, ROCm, ALPAKA) and implement GPU-based inference functionalities in SOFIE. There is already a SYCL implementation for SOFIE, developed in 2023, which can serve as a reference for future development.
+
+## Task ideas
+In this project, the contributor will gain experience with GPU programming and its role in Machine Learning inference. They will start by understanding SOFIE and running inference on CPUs. After researching GPU stacks and methods of their integration with SOFIE, the contributor will implement GPU support for inference, ensuring the code is efficient and well-integrated with GPU technologies.
+
+## Expected results and milestones
+ * **Familiarization with TMVA SOFIE**: Understanding the SOFIE architecture, working with its internals, and running inference on CPUs.
+ * **Research and Evaluation**: Analyzing various GPU stacks (CUDA, ROCm, ALPAKA, etc.) and determining their alignment with SOFIE.
+ * **Implementation of GPU Inference**: Developing functionalities for GPU-based inference in SOFIE.
+ * **[Optional] Benchmarking**: Evaluating the performance of the new GPU functionality by benchmarking memory usage, execution time, and comparing results with other frameworks (such as TensorFlow or PyTorch). 
+
+## Requirements
+  * Proficiency in C++ and Python.
+  * Knowledge of GPU programming (e.g., CUDA).
+  * Familiarity with version control systems like Git/GitHub.
+
+## Mentors
+  * **[Lorenzo Moneta](mailto:[email protected])**
+  * [Sanjiban Sengupta](mailto:[email protected])
+
+## Links
+  * [ROOT Project homepage](https://root.cern/)
+  * [ROOT Project repository](https://github.com/root-project/root)
+  * [SOFIE Repository](https://github.com/root-project/root/tree/master/tmva/sofie)
+  * [Implementation of SOFIE-SYCL](https://github.com/root-project/root/pull/13550/)
+  * [Accelerating Machine Learning Inference on GPUs with SYCL](https://dl.acm.org/doi/10.1145/3648115.3648123)

_gsocproposals/2025/proposal_TMVA-SOFIE-HLS4ML.md

@@ -0,0 +1,41 @@
+---
+title: TMVA SOFIE - HLS4ML Integration for Machine Learning Inference
+layout: gsoc_proposal
+project: ROOT
+year: 2025
+organization: CERN
+difficulty: medium
+duration: 350
+mentor_avail: Flexible
+---
+
+# Description
+SOFIE (System for Optimized Fast Inference code Emit) is a Machine Learning Inference Engine within TMVA (Toolkit for Multivariate Data Analysis) in ROOT. SOFIE offers a parser capable of converting ML models trained in Keras, PyTorch, or ONNX format into its own Intermediate Representation, and generates C++ functions that can be easily invoked for fast inference of trained neural networks. Using the IR, SOFIE can produce C++ header files that can be seamlessly included and used in a 'plug-and-go' style.
+
+Currently, SOFIE supports various machine learning operators defined by ONNX standards, as well as a Graph Neural Network implementation. It supports parsing and inference of Graph Neural Networks trained using DeepMind Graph Nets.
+
+As SOFIE evolves, there is a growing need for inference capabilities on models trained across a variety of frameworks. This project will focus on integrating hls4ml in SOFIE, thereby enabling generation of C++ inference functions on models parsed by hls4ml. 
+
+## Task ideas
+In this project, the contributor will gain experience with C++ and Python programming, hls4ml, and their role in machine learning inference. The contributor will start by familiarizing themselves with SOFIE and running inference on CPUs. After researching the possibilities for integration with hls4ml, they will implement functionalities that ensure efficient inference of ML models parsed by hls4ml, which were previously trained in external frameworks like TensorFlow and PyTorch.
+
+## Expected results and milestones
+ * **Familiarization with TMVA SOFIE**: Understanding the SOFIE architecture, working with its internals, and running inference on CPUs.
+ * **Research and Evaluation**: Exploring hls4ml, its support for Keras and PyTorch, and possible integration with SOFIE.
+ * **Integration with hls4ml**: Developing functionalities for running inference on models parsed by hls4ml.
+
+## Requirements
+  * Proficiency in C++ and Python.
+  * Knowledge of hls4ml
+  * Familiarity with version control systems like Git/GitHub.
+
+## Mentors
+  * **[Lorenzo Moneta](mailto:[email protected])**
+  * [Sanjiban Sengupta](mailto:[email protected])
+
+## Links
+  * [ROOT Project homepage](https://root.cern/)
+  * [ROOT Project repository](https://github.com/root-project/root)
+  * [SOFIE Repository](https://github.com/root-project/root/tree/master/tmva/sofie)
+  * [hls4ml documentation](https://fastmachinelearning.org/hls4ml/)
+  * [hls4ml Repository](https://github.com/fastmachinelearning/hls4ml)

gsoc/2025/mentors.md

@@ -19,9 +19,11 @@ layout: plain
 * David Lange [[email protected]](mailto:[email protected]) CompRes
 * Serguei Linev [[email protected]](mailto:[email protected]) GSI 
 * Peter McKeown [[email protected]](mailto:[email protected]) CERN
+* Lorenzo Moneta [[email protected]](mailto:[email protected]) CERN
 * Felice Pantaleo [[email protected]](mailto:[email protected]) CERN
 * Giacomo Parolini [[email protected]](mailto:[email protected]) CERN
 * Alexander Penev [[email protected]](mailto:[email protected]) CompRes/University of Plovdiv, BG
+* Sanjiban Sengupta [[email protected]](mailto:[email protected]) CERN/UofManchester
 * Mayank Sharma [[email protected]](mailto:[email protected]) UMich
 * Simon Spannagel [[email protected]](mailto:[email protected]) DESY
 * Graeme Stewart [[email protected]](mailto:[email protected]) CERN