Abstract Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly …
Sparse matrix-vector multiplication (SpMV) is an important primitive across a wide range of application domains such as scientific computing and graph analytics. Due to its intrinsic …
S Narayanamoorthy, NR Satish, A Suprun… - US Patent …, 2020 - Google Patents
Disclosed embodiments relate to an accelerator for sparse dense matrix instructions. In one example, a processor to execute a sparse-dense matrix multiplication instruction, includes …
J Pavon, IV Valdivieso, A Barredo… - … Symposium on High …, 2021 - ieeexplore.ieee.org
Sparse matrix operations are critical kernels in multiple application domains such as High Performance Computing, artificial intelligence and big data. Vector processing is widely …
Many accidents and serious problems occur on the road due to the rapid increase in traffic congestion in all sections of the country. Autonomous vehicles provide a solution to …
X Hu, H Huang, X Li, X Zheng, Q Ren, J He… - ACM Transactions on …, 2023 - dl.acm.org
Deep convolutional neural networks (DNNs) have been widely used in many applications, particularly in machine vision. It is challenging to accelerate DNNs on embedded systems …
X Liu, W Li, J Huo, L Yao, Y Gao - Proceedings of the AAAI Conference on …, 2020 - aaai.org
Deep neural network compression is important and increasingly developed especially in resource-constrained environments, such as autonomous drones and wearable devices …
Abstract Convolutional Neural Networks (CNNs) currently dominate the fields of artificial intelligence and machine learning due to their high accuracy. However, their computational …
Q Li, Q Xiao, Y Liang - IECON 2017-43rd Annual Conference of …, 2017 - ieeexplore.ieee.org
Deep learning is nowadays one of the most popular research topics in computer science. In recent years, the extensive application of convolutional neural network has made it become …