Summary
The evolution of Artificial Intelligence-enabled Radio Access Networks(AI-RAN) is transforming traditional cellular base stations into distributedcomputing platforms that integrate real-time radio communication, networkoptimization, and AI-driven edge services. These platforms rely onheterogeneous hardware, including CPUs, GPUs, specialized accelerators,high-bandwidth memory, and high-speed interconnects. However, the coexistenceof latency critical radio functions and dynamic, compute and memory intensiveAI workloads present significant challenges in performance predictability,resource utilization and energy efficiency.
This internship aims to characterize representative AI-RAN workloads onexisting CPU/GPU/accelerator platforms to identify performance bottlenecks andderive hardware architecture requirements for future AI-RAN nodes. Usingopen-source software and experimental performance profiling, the student willinvestigate workload execution, resource utilization, memory behaviour, datamovement, and interference between concurrent workloads. The collectedmeasurements will support a data-driven assessment of existing hardwareconfigurations, highlighting their limitations and potential improvements. Theexpected outcome is a workload-to-architecture framework that translatesobserved performance bottlenecks into recommendations for future heterogeneouscomputing architectures, supporting more efficient, predictable, and scalableAI-RAN infrastructure.
Desirable skills
Type of internship: PhD internship, Master internship
Duration: 6
Required educational background: Computer Science, Electrotechnics/Electrical Engineering, Other
University promotor: Debjoti Bhattacharjee (KULeuven)
Supervising scientist(s): For further information or for application, please contact Udari De Alwis (< correo electrónico eliminado por razones de seguridad >)
The reference code for this position is 2026-INT-319. Mention this reference code in your application.
Imec allowance will be provided.
Applications should include the following information:
Incomplete applications will not be considered.
The evolution of Artificial Intelligence-enabled Radio Access Networks(AI-RAN) is transforming traditional cellular base stations into distributedcomputing platforms that integrate real-time radio communication, networkoptimization, and AI-driven edge services. These platforms rely onheterogeneous hardware, including CPUs, GPUs, specialized accelerators,high-bandwidth memory, and high-speed interconnects. However, the coexistenceof latency critical radio functions and dynamic, compute and memory intensiveAI workloads present significant challenges in performance predictability,resource utilization and energy efficiency.
This internship aims to characterize representative AI-RAN workloads onexisting CPU/GPU/accelerator platforms to identify performance bottlenecks andderive hardware architecture requirements for future AI-RAN nodes. Usingopen-source software and experimental performance profiling, the student willinvestigate workload execution, resource utilization, memory behaviour, datamovement, and interference between concurrent workloads. The collectedmeasurements will support a data-driven assessment of existing hardwareconfigurations, highlighting their limitations and potential improvements. Theexpected outcome is a workload-to-architecture framework that translatesobserved performance bottlenecks into recommendations for future heterogeneouscomputing architectures, supporting more efficient, predictable, and scalableAI-RAN infrastructure.
Desirable skills
- Programming: CUDA, C/C++.Python, GPU execution models,
- Telecommunications: Understanding about 4G/5G RAN architecture, PHY processing, radio resource management, or O-RAN concepts. (Exposure to Open-source RAN software like OpenAirInterface, srsRAN, or related platforms is a plus point.)
- AI/ML workloads: PyTorch, neural network inference, model optimization, or AI acceleration.
- Performance analysis: Linux perf, hardware counters, memory-bandwidth analysis, latency profiling, or power measurement, NVIDIA Nsight Systems, or Nsight Compute
Type of internship: PhD internship, Master internship
Duration: 6
Required educational background: Computer Science, Electrotechnics/Electrical Engineering, Other
University promotor: Debjoti Bhattacharjee (KULeuven)
Supervising scientist(s): For further information or for application, please contact Udari De Alwis (< correo electrónico eliminado por razones de seguridad >)
The reference code for this position is 2026-INT-319. Mention this reference code in your application.
Imec allowance will be provided.
Applications should include the following information:
- resume
- motivation
- current study
Incomplete applications will not be considered.
