PHD in Edge AI and Vision System

Description

The “Integrated and Wireless Systems” Business Unit is looking for a PHD student in the area of Sparse recurrent accelerator for efficient vision – N ear Pixel Array Compute. The project explores hardware-software co-design and symbolic integration for next-generation edge AI. The candidate will contribute to developing energy-efficient accelerators for real-time vision applications.

Responsibilities

• Develop novel hardware prototypes of neural architectures on FPGA or similar platforms.
• Conduct benchmarking and performance analysis.
• Conduct in-depth research to advance the state-of-the-art in deep learning accelerators, exploring novel approaches and solutions.
• Enables state of art approaches overcoming memory transfer bandwidth.
• Work on projects with the primary goal of transferring developed technologies and innovations to the industry, bridging the gap between academia and practical applications.
• Disseminate research findings by publishing in top-tier academic journals and presenting work at prestigious conferences, contributing to the field's knowledge and recognition.
• Support proposal writing.
• Supervision of master students.

Qualifications

• Master’s degree in Computer Science, Electrical Engineering, Machine Learning, or a related field.
• Experience in QAT and post-training quantization.
• Experience in Computer Vision Algorithms.
• Model Compression and Simplification: -Techniques like quantization, which reduces the precision of the numbers used in the model, and pruning, which removes redundant or non-critical parts of a model. - Development of compact neural network architectures that require less computational power for training and inference.
• Strong background in machine learning algorithms and deep learning.
• Familiarity with hardware design and/or accelerator (FPGA, TPU, microcontrollers).
• Research Aptitude: demonstrated research experience through coursework or prior projects and show eagerness to engage in research activities and a willingness to learn and contribute to ongoing projects.
• Academic Excellence: strong academic record, with a history of outstanding performance in relevant coursework.

Interpersonal skills

• Natural curiosity, adaptability, collaborative approach.
• Autonomous, innovative thinker, problem-solving abilities, results oriented.
• Strong organisation and communication skills.
• Fluency in English.