Intelligent Computing & Codesign Lab

ICCL Members in front of Engineering Building 2, on a beautiful day in May.

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Internship Positions at ICCL, UNIST

The Embedded Computing Laboratory at UNIST is recruiting 
multiple undergraduate researchers in the broad area of 
brain-inspired computing as described below.

FPGA-based Deep Learning

Recent advances in deep learning are in large part due to the increased computing capability of off-the-shelf processors. To enable further advances in this direction, this project explores the use of “programmable hardware”, or FPGA (Field-Programmable Gate Array) technology, for the acceleration of deep neural networks such as convolutional neural networks. In a broader context, this research topic is about the application of hardware-software co-design principles to machine learning algorithms, which has many implications and is in active research nowadays.

This topic is best suited for students majoring in both CSE and EE (it doesn’t matter whichever is the 1st major). Prerequisites include Computer Organization, and exposure to hardware description languages is a strong plus. Knowledge of Machine Learning or Artificial Intelligence is a plus, but not required.

Stochastic Deep Neural Network

Creating a Deep Neural Network (DNN) processor has many appeals. A DNN processor can be much more efficient than CPU/GPU/FPGA-based implementations, thus enabling a host of interesting applications (e.g., real-time image recognition), and being a processor, it can be applied to many different neural network applications. Challenges however include how to make it scalable to large and small networks. One idea is to apply Stochastic Computing (SC). SC is a new way of representing numbers and performing arithmetic operations, and radically different from conventional digital computing and enables much more compact implementations of complex functions.

Best candidates for this topic should have strong math skills (especially in probability). Machine learning or hardware design is not a requirement.

Interested students should contact Prof. Lee.

Note: These research positions are related to Samsung Future Technology Project.

Sponsored by Samsung Future Technology Center,

we have launched a new, ambitious project on brain-inspired computing,

titled Reconfigurable Deep Neural Network Processor Based on Stochastic Computing.