Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness in applications relying on embedded devices, where memory is often a scarce resource. Recently, there has been significant progress in the field of image classification on such memory-constrained devices, with novel contributions like the ProtoNN, Bonsai and FastGRNN algorithms. These have been shown to reach up to 98.2% accuracy on optical character recognition using MNIST-10, with a memory footprint as little as 6KB. However, their potential on more complex multi-class and multi-channel image classification has yet to be determined. In this paper, we compare CNNs with ProtoNN, Bonsai and FastGRNN when applied to 3-channel image classification using CIFAR-10. For our analysis, we use the existing Direct Convolution algorithm to implement the CNNs memory-optimally and propose new methods of adjusting the FastGRNN model to work with multi-channel images. We extend the evaluation of each algorithm to a memory size budget of 8KB, 16KB, 32KB, 64KB and 128KB to show quantitatively that Direct Convolution CNNs perform best for all chosen budgets, with a top performance of 65.7% accuracy at a memory footprint of 58.23KB.
Target-Guided Bayesian Flow Networks for Quantitatively Constrained CAD Generation
College of Computer Science, Sichuan University（四川大学计算机科学学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, Chengdu, China（教育部机器学习与工业智能工程研究中心）
CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration
University of Houston（休斯顿大学）；University of California, Berkeley（加州大学伯克利分校）；University of Pennsylvania（宾夕法尼亚大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation
Computer Vision Center（计算机视觉中心）；UAB（巴塞罗那自治大学）；Cambridge University（剑桥大学）；iRobot Corporation（iRobot公司）；Corporate Research & Development Center, Toshiba Corporation（东芝公司企业研发中心）
A dynamic memory assignment strategy for dilation-based ICP algorithm on embedded GPUs
School of Coumputing Institute of Science（计算学院科学研究所）；Dalian University of Technology（大连理工大学）；University of Wollongong (College HK)（卧龙岗大学（香港学院））；School of Computing Institute of Science（计算学院科学研究所）
Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging
Beijing Normal University（北京师范大学）；Beijing Friendship Hospital, Capital Medical University（首都医科大学北京友谊医院）；The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部附属第一医院）；Jiangnan University（江南大学）；University of Melbourne（墨尔本大学）
