Customized Computing for Understanding the Brain and Brain-Inspired Computing

Project status: 

Recent work from this project got the Best Paper Award in ISLPED'18.

Moore’s law has driven the exponential growth of information technology for more than 50 years, during which the ever-increased computing power has had a huge impact on people’s lives. AI algorithms such as reinforcement learning can help super powerful computers beat humans in specific task like board gaming. However, computing machines based on state-of-the-art software, hardware and manufacturing technology still cannot match human brains in many aspects, such as lifelong unsupervised learning, and energy processing efficiency. Some researchers believe that figuring out how brain works is one of the most challenging big questions in the 21st century, and our understanding in this area is still at an early stage.

Modern fluorescence microscopy technologies open new ways to observe brain neural network in vivo at unprecedented spatial and temporal resolution, and provide a lot of new research opportunities for making experiments and understanding how brain works. We are working with leading team in this field at UCLA and focus on the backend real-time processing for the calcium imaging data collected from a 3-gram head mounted miniature fluorescence microscope, as the figure shows. Technical challenges we are facing include: Acceleration for real-time calcium image analysis, which consists of pipelined processing stages such as motion correction, cell segmentation and neural activity extraction; Energy-efficient embedded computing on miniaturized device with limited power supply.


Broader impacts from this research project shed light on developing energy-efficient computing architecture, establishing real-time closed-loop feedback control system and accelerating the pace of neuroscience research and education worldwide.

This project is a collaboration with Psychology Department and Department of Neurology, UCLA.


1. H.T. Blair, J. Cong, D. Wu. FPGA Simulation Engine for Customized Construction of Neural Microcircuit. Proceedings of the 2013 International Conference on Computer-Aided Design (ICCAD), pp. 607-614, November 2013.

2. Zhe Chen, Andrew Howe, Hugh T. Blair, Jason Cong. CLINK: Compact LSTM Inference Kernel for Energy Efficient Neurofeedback Devices, International Symposium on Low Power Electronics and Design (ISLPED), July 23-25, 2018. (Best Paper Award)

3. Zhe Chen, Hugh T. Blair, Jason Cong. LA-NoRMCorre: LSTM-Assisted Non-Rigid Motion Correction on FPGA for Calcium Image Stabilization, 27th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA), Feb 24-26, 2019. To be appeared.


Hugh T. Blair

Peyman Golshani

News: National Science Foundation will help UCLA spread technology behind miniscope.

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