Executive Development Programme in Hands-On Deep Learning with FPGA Implementation

This course is designed for technical leaders and engineers with a background in machine learning and a desire to implement deep learning models using FPGA technology. Participants will gain hands-on experience in deploying deep learning models on FPGAs, optimizing performance, and understanding the hardware-software interface required for efficient implementation.

Attendees will learn to use tools and frameworks for deep learning inference on FPGAs, analyze trade-offs between accuracy and latency, and develop strategies for integrating FPGA-based solutions into existing systems. By the end, participants will be capable of leading projects that leverage FPGAs for accelerated deep learning applications.

1. 1. Introduction to Deep Learning: Learners will understand the basics of deep learning, including neural networks, activation functions, and backpropagation. They will gain foundational skills in Python programming for deep learning.
2. 2. Convolutional Neural Networks (CNNs): This module covers the architecture and applications of CNNs, focusing on image recognition tasks. Learners will implement CNNs using TensorFlow and Keras, enhancing their ability to process and analyze visual data.
3. 3. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM): Learners will explore RNNs and LSTMs for sequence data processing, including text and time-series analysis. Practical skills include building and training RNN models to predict sequences.
4. 4. Deep Learning Frameworks and Tools: This module introduces popular deep learning frameworks such as TensorFlow, PyTorch, and ONNX, along with tools for model evaluation and deployment. Learners will gain hands-on experience with these tools to optimize and deploy deep learning models.
5. 5. Introduction to FPGAs: Learners will learn about Field-Programmable Gate Arrays (FPGAs) and their role in accelerating deep learning tasks. They will understand the hardware architecture and programming models specific to FPGAs.
6. 6. FPGA-Based Deep Learning Acceleration: This module covers the process of porting deep learning models to FPGAs, including model quantization, hardware acceleration, and integration with software frameworks. Practical skills include using Xilinx or Intel FPGA development tools.
7. 7. Practical FPGA Implementations: Learners will apply their knowledge to implement and optimize deep learning models on FPGAs. They will work on case studies and projects to gain experience in deploying deep learning on edge devices using FPGAs.
8. 8. Advanced Topics in Deep Learning: This module delves into advanced topics such as transfer learning, generative models, and adversarial networks. Learners will explore cutting-edge research and implement these models in Python, enhancing their ability to solve complex problems.
9. 9. FPGA Design and Verification: Learners will learn about the design and verification process for FPGAs, including simulation, synthesis, and testing. Practical skills include using tools like ModelSim and Vitis for FPGA design validation.
10. 10. Project and Capstone: Learners will work on a comprehensive project that integrates deep learning models with FPGA implementations. They will apply their knowledge and skills to solve real-world problems, culminating in a final presentation and report.