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https://doi.org/10.2139/ssrn.4676008

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inferredcites →https://doi.org/10.1007/978-3-030-57675-2_16openalex-extracted
Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs
inferredcites →https://doi.org/10.1007/s10586-016-0611-8openalex-extracted
Architecture-aware configuration and scheduling of matrix multiplication on asymmetric multicore processors
inferredcites →https://doi.org/10.1007/s11042-020-09634-7openalex-extracted
A comprehensive survey on convolutional neural network in medical image analysis
inferredcites →https://doi.org/10.1007/s11227-021-03673-zopenalex-extracted
PyDTNN: A user-friendly and extensible framework for distributed deep learning
inferredcites →https://doi.org/10.1007/s11227-022-05003-3openalex-extracted
Micro-kernels for portable and efficient matrix multiplication in deep learning
inferredcites →https://doi.org/10.1007/s11227-023-05088-4openalex-extracted
Efficient and portable Winograd convolutions for multi-core processors
inferredcites →https://doi.org/10.1016/j.measen.2021.100080openalex-extracted
A survey of Convolutional Neural Networks —From software to hardware and the applications in measurement
inferredcites →https://doi.org/10.1016/j.sysarc.2022.102806openalex-extracted
Reformulating the direct convolution for high-performance deep learning inference on ARM processors
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-extracted
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.435openalex-extracted
Fast Algorithms for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/ipdps.2014.110openalex-extracted
Anatomy of High-Performance Many-Threaded Matrix Multiplication
inferredcites →https://doi.org/10.1109/jproc.2017.2761740openalex-extracted
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
inferredcites →https://doi.org/10.1109/sbac-pad49847.2020.00023openalex-extracted
High Performance and Portable Convolution Operators for Multicore Processors
inferredcites →https://doi.org/10.1109/sc.2018.00069openalex-extracted
Anatomy of High-Performance Deep Learning Convolutions on SIMD Architectures
inferredcites →https://doi.org/10.1109/tnnls.2021.3084827openalex-extracted
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
inferredcites →https://doi.org/10.1145/1356052.1356053openalex-extracted
Anatomy of high-performance matrix multiplication
inferredcites →https://doi.org/10.1145/2755561openalex-extracted
The BLIS Framework
inferredcites →https://doi.org/10.1145/2925987openalex-extracted
Analytical Modeling Is Enough for High-Performance BLIS
inferredcites →https://doi.org/10.1145/3079856.3080246openalex-extracted
In-Datacenter Performance Analysis of a Tensor Processing Unit
inferredcites →https://doi.org/10.1145/3140659.3080246openalex-extracted
In-Datacenter Performance Analysis of a Tensor Processing Unit
inferredcites →https://doi.org/10.1145/3330345.3330382openalex-extracted
The anatomy of efficient FFT and winograd convolutions on modern CPUs
inferredcites →https://doi.org/10.1145/3624062.3624229openalex-extracted
Automatic Generation of Micro-kernels for Performance Portability of Matrix Multiplication on RISC-V Vector Processors
inferredcites →https://doi.org/10.21203/rs.3.rs-1909301/v1openalex-extracted
inferredcites →https://doi.org/10.21203/rs.3.rs-3388260/v1openalex-extracted
inferredcites →https://doi.org/10.4230/oasics.ng-res.2024.2openalex-extracted
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
inferredcites →https://doi.org/10.48550/arxiv.1809.07851openalex-extracted
FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why
inferredcites →https://doi.org/10.48550/arxiv.1904.05717openalex-extracted
The MOMMS Family of Matrix Multiplication Algorithms
inferredcites →https://doi.org/10.5555/3291656.3291744openalex-extracted
Anatomy of high-performance deep learning convolutions on SIMD architectures
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