Customized Computing for Brain Research and Brain-Inspired Computing

Project status: 

Recent work in this project got the Best Paper Award in ISLPED'18, and Pearl Cohen Poster Award in 2019 UCLA Bioscience Innovation Day.

Moore's law has driven the exponential growth of computing in past decades. Meanwhile, the research pace in understanding how brain works and breakthroughs in neuroscience have never stopped. Under this big picture, this project focuses on creating high performance and energy efficient computation hardware and systems for supporting neuroscientific research, and developing energy-efficient brain-inspired computing paradigm and architectures. 

Miniaturized fluorescence microscope is an emerging technique in observing the activity of a large population of neuros in vivo. It opens up new research opportunities for neuroscientific 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.

Recent advancements in microscopes allowing imaging an entire brain of a mouse in a couple days. However due to the intensive resolution and scale, computational processing of these images can take months. Although the results of these computational pipelines are analyzed offline after imaging, this processing can severely limits the workflow and hypothesis iteration speed. In our collaboration project with PIs Dr. William Yang (UCLA Neuroscience) and Dr. Hongwei Dong (USC Neuroscience) that was awarded an NIH U01 BRAIN grant, we accelerate such processing to study brain cell features of Huntington’s disease. The major run-time costs in these pipelines arise from converting dense three-dimensional images to a set of edges and nodes. These graphs offer a sparse and natural representation of neurons which have collections of branches whose pattern and connections determine their function or pathology. Graph processing algorithms are known for their acceleration difficulty due to their low computation to memory access ratio and their load-balance irregularity during parallelization. It is also increasingly more common for image pipelines to require additional specific image segmentation tasks such as cell body detection which are accomplished with more common neural network approaches. At a modest 30x magnification of a single mouse brain, the data size is still 20-30 teravoxels, therefore we focus on designs that optimize data-flow and prioritize computation at the edge, for example on the imaging workstation itself. 

Broader impacts from these research projects shed light on establishing real-time closed-loop feedback control system, speeding up neural reconstruction of the entire brain of mouse, developing energy-efficient computing architecture, and accelerating the pace of neuroscience research and education worldwide.


[1] Zhe Chen, Garrett J. Blair, Hugh T. Blair, Jason Cong. BLINK: Bit-Sparse LSTM Inference Kernel Enabling Efficient Calcium Trace Extraction for Neurofeedback DevicesInternational Symposium on Low Power Electronics and Design (ISLPED), Aug. 2020.

[2] Zhe Chen, Hugh T. Blair, Jason Cong. LANMC: LSTM-Assisted Non-Rigid Motion Correction on FPGA for Calcium Image Stabilization, 27th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2019.

[3] Zhe Chen, Andrew Howe, Hugh T. Blair, Jason Cong. CLINK: Compact LSTM Inference Kernel for Energy Efficient Neurofeedback DevicesInternational Symposium on Low Power Electronics and Design (ISLPED), Jul. 2018. (Best Paper Award)

[4] H.T. Blair, A. Wu, J. Cong. Oscillatory Neurocomputing with Ring Attractors: A Network Architecture for Mapping Locations in Space onto Patterns of Neural Synchrony. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 369(1635), 20120526, 2014.

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


Hugh T. Blair

Peyman Golshani

William Yang

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

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