The resurgence of near-memory processing (NMP) with the advent of big data has shifted the computation paradigm from processor-centric to memory-centric computing. To meet the bandwidth and capacity demands of memory-centric computing, 3D memory has been adopted to form a scalable memory-cube network. Along with NMP and memory system development, the mapping for placing data and guiding computation in the memory-cube network has become crucial in driving the performance improvement in NMP. However, it is very challenging to design a universal optimal mapping for all applications due to unique application behavior and intractable decision space. In this paper, we propose an artificially intelligent memory mapping scheme, AIMM, that optimizes data placement and resource utilization through page and computation remapping. Our proposed technique involves continuously evaluating and learning the impact of mapping decisions on system performance for any application. AIMM uses a neural network to achieve a near-optimal mapping during execution, trained using a reinforcement learning algorithm that is known to be effective for exploring a vast design space. We also provide a detailed AIMM hardware design that can be adopted as a plugin module for various NMP systems. Our experimental evaluation shows that AIMM improves the baseline NMP performance in single and multiple program scenario by up to 70% and 50%, respectively.
Continual Learning via Inter-Task Synaptic Mapping
Huazhong University of Science and Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）；Singapore Institute of Manufacturing Technology, A*Star（新加坡科技研究局新加坡制造技术研究院）
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers
Warsaw University of Technology（华沙技术大学）；Computer Vision Center, Barcelona（巴塞罗那计算机视觉中心）；IDEAS Research Institute（IDEAS研究机构）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；Xi’an Jiaotong University（西安交通大学）；Nankai University（南开大学）
CM2-Net: Continual Cross-Modal Mapping Network for Driver Action Recognition
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Continental-NTU Corporate Lab, Nanyang Technological University（大陆集团-南洋理工大学联合企业实验室）；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）
Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；City University of Hong Kong（香港城市大学）；School of Mathematics and Statistics, Xidian University（西安电子科技大学数学与统计学院）
Continual Learning of Context-dependent Processing in Neural Networks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）
cAPM: Continual AI-Assisted Pace-Mapping with Active Learning
Rochester Institute of Technology（罗切斯特理工学院）；University of Utah（犹他大学）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Medical University of Graz（格拉茨医科大学）；University of Pennsylvania Perelman School of Medicine（宾夕法尼亚大学佩雷尔曼医学院）；The University of Arizona College of Medicine（亚利桑那大学医学院）；Dalhousie University（达尔豪斯大学）
A Retention-Centric Framework for Continual Learning with Guaranteed Model Developmental Safety
Department of Computer Science & Engineering, Texas A&M University, College Station, USA（德克萨斯A&M大学计算机科学与工程系）；Department of Mathematics, The University of Iowa, Iowa City, USA（爱荷华大学数学系）；Global Research and Development, General Motors, Warren, USA（通用汽车全球研发）；Department of Electrical and Computer Engineering, The Ohio State University, Columbus, UAS（俄亥俄州立大学电气与计算机工程系）；Tippie College of Business, The University of Iowa, Iowa City, USA（爱荷华大学蒂皮学院）
School of Computer Science and Engineering, Macau University of Science and Technology（澳门科技大学计算机科学与工程学院）；Department of Computer Science, Hong Kong Baptist University（香港浸会大学计算机科学系）
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
Warsaw University of Technology（华沙理工大学）；IDEAS NCBR；Jagiellonian University（雅盖隆大学）；Gdańsk University of Technology（格但斯克理工大学）；Autonomous University of Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；Tooploox
Improving Open-world Continual Learning under the Constraints of Scarce Labeled Data
Southwestern University of Finance and Economics（西南财经大学）；Leiden University（莱顿大学）；JD Intelligent Cities Research（京东智能城市研究院）；Southwest Jiaotong University（西南交通大学）
本文研究开放世界少样本持续学习问题，提出集成 ITA、MOB 和 AKS 的 OFCL 框架，以缓解遗忘、过拟合、开放检测及未知向已知更新难题。
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
The University of Queensland（昆士兰大学）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；University of Electronic Science and Technology of China（电子科技大学）
BM-CL: Bias Mitigation through the lens of Continual Learning
Research Institute for Signals, Systems and Computational Intelligence, sinc(i), CONICET - Universidad Nacional del Litoral（信号、系统与计算智能研究所以及CONICET - 国家沿海大学）；AI Development and Evaluation (AIDE) Lab, Stanford University（人工智能发展与评估实验室，斯坦福大学）；Institute of Computer Sciences (ICC), CONICET - Universidad de Buenos Aires（计算机科学研究所（ICC），CONICET - 阿根廷布宜诺斯艾利斯大学）
Continual Learning of Feedback-based Molecular Communication
Department of Computer Science, University of Massachusetts, Boston（马萨诸塞大学计算机科学系）；Graduate School of Informatics, Osaka Metropolitan University（大阪 metropolitan 大学信息研究生院）
Federated Continual Learning: Concepts, Challenges, and Solutions
Faculty of Computer Science, University of New Brunswick（新 Brunswick大学计算机科学学院）；Department of Electrical and Computer Engineering, University of Windsor（温莎大学电气与计算机工程系）
