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Institute of Information Science

Postdoctoral Researcher 2 Person

Research on Optimization of Deep Learning Model Inference and Training

The Computer Systems Laboratory - Machine Learning Systems team focuses on research areas including parallel and distributed computing, compilers, and computer architecture. We aim to leverage computer system technologies to accelerate the inference and training of deep learning models and develop optimizations for next-generation AI models. Our research emphasizes the following:

1. AI Model Compression and Optimization
Model compression techniques (e.g., pruning and quantization) reduce the size and computational demands of AI models, which are crucial for resource-constrained platforms such as embedded systems and memory-limited AI accelerators. We aim to explore:
* AI compiler: deployment methods for compressed models across servers, edge devices, and heterogeneous systems.
* High performance computing: efficient execution of compressed models on processors with advanced AI extensions, e.g., Intel AVX512, ARM SVE, RISC-V RVV, and tensor-level accelerations on GPUs and NPUs.

2. AI Accelerator Design
We aim to design AI accelerators for accelerating AI model inference, focusing on software and hardware co-design and co-optimization.

3. Optimization of AI Model Inference in Heterogeneous Environments
Computer architectures are evolving toward heterogeneous multi-processor designs (e.g., CPUs + GPUs + AI accelerators). Integrating heterogeneous processors to execute complex models (e.g., hybrid models, multi-models, and multi-task models) with high computational efficiency poses a critical challenge. We aim to explore:
* Efficient scheduling algorithms.
* Parallel algorithms for the three dimensions: data parallelism, model parallelism, and tensor parallelism.

- Ph.D. degree in Computer Science, Computer Engineering, or Electrical Engineering
- Experience in parallel computing and parallel programming (CUDA or OpenCL, C/C++ programming) or hardware design (Verilog or HLS)
- Proficient in system and software development

Candidates with the following experience will be given priority:
- Experience in deep learning platforms, including PyTorch, TensorFlow, TVM, etc.
- Experience in high-performance computing or embedded systems.
- Experience in algorithm designs.
- Knowledge of compilers or computer architecture

8:30AM-5:30PM

Institute of Information Science, Academia Sinica

According to Academia Sinica standards: Postdoctoral Researchers: NT$64,711-99,317/month. Benefits include: labor and healthcare insurance, and year-end bonuses.

Dr. Ding-Yong Hong

Room 818, New IIS Building, Academia Sinica

02-27883799 ext. 1818

Please email your CV (including publications, projects, and work experience), transcripts (undergraduate and above), and any other materials that may assist in the review process to the following PIs:
- Dr. Ding-Yong Hong: dyhong@iis.sinica.edu.tw
- Dr. Jan-Jan Wu: wuj@iis.sinica.edu.tw

2025-01-20

2025-12-31