北京大学姜明教授学术报告通知，欢迎感兴趣的师生参加！
报告题目：Recent advances in accelerating x-ray tomography reconstruction with Mumford-Shah regularization
报 告 人：姜明 （School of Mathematical Sciences, Peking University）
时 间：2017.04.13周四上午9:30
地 点：东南大学生物电子学实验室三楼会议室
联 系 人：罗守华
简 介：姜明，北京大学数学学院信息科学系教授。从1980年至1989年在北京大学数学系学习，获博士学位。从1989 年到1995年在北京理工大学应用数学系工作。1996 年到1997 年在意大利International Centre of Theoretical Physics, Microprocessor Laboratory进行研究工作。1998年至今在北京大学数学科学学院信息科学系工作。目前是期刊“Sensing and Imaging”的共同主编，是“Inverse Problems”，“BioMedical Engineering OnLine”，“Signal Processing”等期刊编委。对图像重建迭代算法、生物萤光层析成像技术、气溶胶反演问题和多模态成像技术进行了深入研究。与合作者建立了生物萤光层析成像的数学理论，发表了生物萤光层析成像方面的第一篇期刊论文。2004年获得国家杰出青年科学基金。2008年被聘为教育部**学者特聘教授。详细情况请访问个人主页：http://www.math.pku.edu.cn/teachers/jiangm
报告摘要：
In addition to multi-CPU clusters, GPU and DSP, FPGA (field-programmable gate array) is another hardware accelerating approach. High-level synthesis tools from C to FPGA can optimize the implementation under the performance, power, and cost constraints, and enable energy-efficient accelerator-rich architecture. In previous work, we used FPGA for the simultaneous image reconstruction and segmentation with Mumford-shah regularization for XCT under -convergence, and achieved 31X speed-up and 622X energy efficiency compared to CPU implementation. However, FGPA was only used to accelerate the computation of forward and backward projections. Because of the limited memory on chip, recently, we propose asynchronous parallel Kaczmarz (ART) and RAMLA methods with diminishing relaxations. Preliminary results demonstrate better early reconstruction images with both methods. This asynchronous parallel approach fits well with the architecture of FPGA and reduces the communication cost, and is applicable to other parallel architectures in general (e.g. multi-core CPUs). In this talk, we also discuss more general asynchronous parallel data-block and image-block iterative methods with regularization, and approaches to establish their convergence from theoretical perspective. We will report our recent implementation with multi-GPU and also the application to electron tomography. This is a joint work with Jason Cong, Guojie Luo, Peter Maass, Thomas Page, Eric Todd Quinto, Li Shen, and Wentai Zhang.
