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Investigation and Benchmarking of FPGA AI Cores for Lens-Free Imaging Algorithms
Master internship - Leuven | Just now
Lens-free imaging is an emerging computational imaging technique that replaces conventional optical lenses with advanced reconstruction algorithms. By combining diffraction-based image acquisition with high-performance processing, lens-free imaging can enable compact, low-cost, and portable imaging systems for applications such as biomedical diagnostics, industrial inspection, environmental monitoring, and edge AI sensing.
Recent generations of adaptive computing platforms, such as the AMD Xilinx Versal AI Core, integrate programmable logic, embedded processors, high-bandwidth memory interfaces, and dedicated AI Engines. These heterogeneous architectures offer significant potential to accelerate computationally intensive imaging algorithms while reducing power consumption and system latency.
However, the suitability of the Versal AI Core architecture for lens-free imaging workloads has not yet been systematically evaluated. Understanding the performance, scalability, and energy efficiency of different implementation approaches is essential for future imaging systems.
Objectives
The objective of this thesis is to investigate and benchmark the use of the Xilinx Versal AI Core platform for accelerating lens-free imaging algorithms.
The student will:
- Analyze the computational requirements of representative reconstruction algorithms for lens-free imaging.
- Investigate the Versal AI Core architecture, including:
- AI Engines
- Programmable Logic (FPGA fabric)
- Embedded processing subsystem
- Memory architecture and data movement
- Implement selected lens-free imaging algorithms on the Versal platform.
- Evaluate different acceleration approaches and partitioning strategies.
- Benchmark performance against CPU and/or GPU reference implementations.
- Identify architectural bottlenecks and optimization opportunities.
Research Questions
The thesis aims to answer questions such as:
- will lens-free imaging algorithms benefit most from Versal AI Core acceleration?
- What performance gains can be achieved compared to conventional CPU or GPU execution?
- How does power efficiency compare across implementation options?
- What are the limitations and challenges when deploying computational imaging pipelines on heterogeneous adaptive SoCs?
Methodology
The student will:
Phase 1: Literature Review
- Review lens-free imaging concepts and applications.
- Investigate the Versal AI Core architecture and development tools.
Phase 2: Algorithm Analysis
- Characterize computational complexity, memory bandwidth, and parallelization opportunities for the selected algorithms.
Phase 3: Hardware Acceleration
- Implement selected kernels on the Versal platform.
- Explore multiple architecture mappings:
- CPU-only
- FPGA acceleration
- AI Engine acceleration
- Hybrid implementations
Phase 4: Benchmarking
- Measure:
- Execution time
- Throughput
- Latency
- Resource utilization
- Power consumption
- Energy per reconstructed image
Phase 5: Evaluation and Recommendations
- Compare implementation approaches.
- Identify best practices for future computational imaging systems.
- Provide design guidelines for deploying lens-free imaging on Versal-based platforms.
Expected Results
The thesis is expected to deliver:
- A comprehensive performance analysis of lens-free imaging workloads on Xilinx Versal AI Core.
- Optimized hardware-accelerated implementations of selected reconstruction algorithms.
- Quantitative benchmarking results against conventional computing platforms.
- Recommendations for future imaging system architectures leveraging adaptive computing technologies.
Required Skills
Preferred background:
- Digital design and FPGA development
- C/C++ programming
- Embedded systems
- Signal processing or image processing
- Basic knowledge of AI/ML acceleration is a plus
Type of internship: Master internship
Duration: 6 months
Required educational background: Computer Science, Other, Electrotechnics/Electrical Engineering
Supervising scientist(s): For further information or for application, please contact Abelardo Gonzalez Diaz (Abelardo.GonzalezDiaz@imec.be)
The reference code for this position is 2026-INT-317. Mention this reference code in your application.
Imec allowance will be provided for students studying at a non-Belgian university.
Applications should include the following information:
- resume
- motivation
- current study
Incomplete applications will not be considered.