Thesis Proposal: Alternative Learning Methods For Tiny Ai Devices - In Collaboration With St Mi[...]

The primary focus of this internship is to investigate and develop forward learning methods tailored for deployment on tiny embedded neural processing units with limited memory and energy budgets. Specifically, the candidate will explore lightweight learning techniques that can mitigate the accuracy loss experienced by pre-trained models when deployed on resource-constrained devices. By leveraging these alternative methods, the aim is to train efficient AI models directly on the target embedded systems. ST Microelectronics has recently introduced new smart sensors and devices integrating small, on-board energy-efficient AI accelerators. However, on such small devices, it becomes power hungry to accommodate the huge computational and memory load of classical backpropagation. Can these inference-only accelerators also be used for training with some novel approaches?

Thesis Work: Literature Review and Research: Conduct an in-depth review of state-of-the-art lightweight learning methods suitable for embedded deployment. Investigate techniques such as Forward-Forward propagation [1], PEPITA [2], and Sparse backpropagation [3]

Model Development, Training, and Optimization: Implement and train lightweight deep learning models using PyTorch, to solve computer vision problems.

Optimize the trained models for deployment on various embedded platforms, considering factors such as memory footprint, computational complexity, and energy efficiency - accounting for the computational overhead of the different training algorithms.

On-Device Learning Implementation: Implement the different updating strategies analyzed during the literature review, comparing the overhead in terms of memory and operations needed.

Compare the models' final accuracy, latency, and resource utilization to identify the most suitable solutions for real-world deployment. Analyze the tradeoff between resource utilization and final accuracy for different levels of hardware resources.

Qualifications: Knowledge of machine learning, deep learning, and computer vision. Programming languages: Python and C/C++. Experience with deep learning frameworks like TensorFlow or PyTorch. Familiarity with embedded systems and hardware platforms is a plus


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