Engineer F/M Dynamic Approximate Computing for Energy-Efficient AI Hardware Accelerators
Inria · Rennes
Research instituteonsite$36k
At a glance
Highlights
- Research dynamic approximate computing to boost energy efficiency of AI accelerators.
- Explore adaptive approximation across algorithm, architecture and circuit layers.
- Prototype and validate designs on deep learning inference and vision workloads.
Ideal candidate
Hardware-oriented engineer with strong computer architecture background and passion for sustainable AI. Analytical problem-solver eager to bridge algorithms and circuits through adaptive co-design.
Requirements
- Strong HW design skills with VHDL/Verilog and HW synthesis flow for FPGA or ASIC
- Strong foundation in computer architecture and systems design
- Strong software programming/scripting in C/C++, Python and Linux scripting
- Familiarity with machine learning fundamentals and PyTorch/TensorFlow
- Experience with approximate computing techniques such as functional approximation, mixed-precision arithmetic and pruning is a plus
- Knowledge of neural network accelerator hardware architectures is a plus
- Excellent analytical and problem-solving abilities with interest in energy efficiency and strong communication skills
Lab profile
About Inria
French national research institute advancing digital sciences, computing, AI and applied mathematics.
What they build
- Digital sciences and computing research
- Artificial intelligence for microscopy and video analysis
- Quantum information theory
Known for
- Joint teams with Paris-Saclay University and Institut Polytechnique de Paris
- Embedded in Paris-Saclay science and technology cluster
- Rich R&D ecosystem with startups, industry and clusters
- International collaborations and multinational scientific staff
Key facts
- Founded in 1967
- Headquartered in France
- Nine research centres across France
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