@article {414, title = {A Review of Algorithm \& Hardware Design for AI-Based Biomedical Applications}, journal = {IEEE Transactions on Biomedical Circuits and Systems }, volume = {14}, year = {2020}, month = {04/2020}, pages = {145-163}, abstract = {

This paper reviews the state of the arts and trends of the AI-based biomedical processing algorithms and hardwares. The algorithms and hardwares for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-based biomedical processor have also been discussed.

}, keywords = {psclab}, issn = {1940-9990}, doi = {10.1109/TBCAS.2020.2974154}, author = {Ying Wei and Jun Zhou and Yin Wang and Yinggang Liu and Qingsong Liu and Jiansheng Luo and Chao Wang and Fengbo Ren and Li Huang} } @article {421, title = {Real time end-to-end learning system for a high frame rate video compressive sensing network}, number = {US16/165,568}, year = {2019}, abstract = {

A real time end-to-end learning system for a high frame rate video compressive sensing network is described. The slow reconstruction speed of conventional compressive sensing approaches is overcome by directly modeling an inverse mapping from compressed domain to original domain in a single forward propagation. Through processing massive unlabeled video data such a mapping is learned by a neural network using data-driven methods. Systems and methods according to this disclosure incorporate a multi-rate convolutional neural network (CNN) and a synthesizing recurrent neural network (RNN) to achieve real time compression and reconstruction of video data.

}, keywords = {psclab}, issn = {US20190124346A1}, url = {https://patents.google.com/patent/US20190124346A1/en?inventor=Fengbo+Ren\&oq=inventor:(Fengbo+Ren)}, author = {Fengbo Ren and Kai Xu} } @inproceedings {Los Angeles, CA, title = {Recognizing terrain features on terrestrial surface using a deep learning model - An example with crater detection}, year = {2017}, month = {11/2017}, pages = {33-36}, publisher = {ACM}, address = {Los Angeles, CA}, abstract = {

This paper exploits the use of a popular deep learning model - the faster-RCNN - to support automatic terrain feature detection and classification using a mixed set of optimal remote sensing and natural images. Crater detection is used as the case study in this research since this geomorphological feature provides important information about surface aging. Craters, such as impact craters, also effect global changes in many aspects, such as geography, topography, mineral and hydrocarbon production, etc. The collected data were labeled and the network was trained through a GPU server. Experimental results show that the faster-RCNN model coupled with a widely used convolutional network ZF-net performs well in detecting craters on the terrestrial surface.

}, keywords = {psclab}, isbn = {978-1-4503-5498-1/17/11}, author = {Wenwen Li and Bin Zhou and Chia-Yu Hsu and YIxing Li and Fengbo Ren} } @inproceedings {kulkarni2016reconnet, title = {Reconnet: Non-iterative reconstruction of images from compressively sensed measurements}, year = {2016}, pages = {449{\textendash}458}, keywords = {Compressive Sensing, ref}, author = {Kulkarni, Kuldeep and Lohit, Suhas and Turaga, Pavan and Kerviche, Ronan and Ashok, Amit} } @article {putnam2015reconfigurable, title = {A reconfigurable fabric for accelerating large-scale datacenter services}, journal = {IEEE Micro}, volume = {35}, number = {3}, year = {2015}, pages = {10{\textendash}22}, publisher = {IEEE}, keywords = {Deep Learning, ref}, author = {Putnam, Andrew and Caulfield, Adrian M and Chung, Eric S and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and others} } @article {20, title = {Reference Calibration of Body-voltage Sensing Circuit for High-speed STT-RAMs}, journal = {IEEE Transactions on Circuits and Systems I: Regular Papers}, volume = {60}, year = {2013}, month = {Apr.}, pages = {2932{\textendash}2939}, abstract = {

With the continuing scaling of MTJ, the high-speed reading of STT-RAM becomes increasingly difficult. Recently, a body-voltage sensing circuit (BVSC) has been proposed for boosting the sensing speed. This paper analyzes the effectiveness of using the reference calibration technique to compensate for the device mismatches and improve the read margin of BVSC. HSPICE simulation results show that a 2-bit reference calibration can improve the worst-case read margin in a 1-Mb memory by over 3 times. This leads to up to 30\% higher yield across all process corners. In order to maintain the yield improvement even in the worst-case corner, independent calibration circuitry has to be deployed for each memory array.

}, keywords = {psclab}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6492140}, author = {Ren, Fengbo and Park, Henry and Yang, C-KK and Markovi{\'c}, Dejan} }