A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al.
This paper provides an end-to-end review of sensor hardware, calibration, and fusion techniques for object detection in autonomous vehicles, highlighting challenges and future research directions.
Gaurav Menghani
A comprehensive survey of efficient deep learning covering modeling techniques, infrastructure, and hardware, with a practical guide for optimizing model training and deployment.
Taiwo Samuel Ajani, Agbotiname Lucky Imoize, Aderemi A. Atayero
A survey of optimization techniques for implementing machine learning algorithms on resource-constrained embedded and mobile devices, covering algorithms, hardware, and applications.
Unknown
This paper surveys the field of embodied AI, covering fundamentals, hardware systems, and the integration of LLMs and multimodal LLMs to create world models.
Yutao Sun, Zhenyu Li, Yike Zhang, et al.
A comprehensive survey of efficient attention mechanisms for Transformers, covering linear and sparse attention methods with hardware considerations.
Boncz, Peter, Manegold, Stefan, Martin Kersten
This paper optimizes large main-memory joins on modern hardware using vertically fragmented data structures and a radix-cluster partitioning algorithm to improve cache and CPU performance.
Luca Benini, Giovanni De Micheli
This tutorial surveys system-level design methods for energy-efficient electronic systems, covering hardware and software energy reduction across design phases.
D. Du, Gu Gong, Xiaowen Chu
A comprehensive survey of model quantization and hardware acceleration techniques for vision transformers.
Sangmin Park, Young‐Gab Kim
This paper presents a comprehensive taxonomy of the Metaverse, dividing it into three components (hardware, software, contents) and three approaches (user interaction, implementation, application), and analyzes essential techniques and open challenge
Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra, et al.
This paper presents an AI/TP system that automatically proves about 60% of Mizar theorems in hammer setting and 75% with human-written premise hints.