On the public citation graph
The anatomy of efficient FFT and winograd convolutions on modern CPUs
0 signed · 52 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-63390-9_2openalex-snapshot
Ascertaining Uncertainty for Efficient Exact Cache Analysis
inferredcites →https://doi.org/10.1016/b978-0-12-809194-4.00002-8openalex-snapshot
Knights Landing overview
inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.435openalex-snapshot
Fast Algorithms for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/hotchips.2008.7476531openalex-snapshot
The roofline model: A pedagogical tool for program analysis and optimization
inferredcites →https://doi.org/10.1109/icassp.1998.681704openalex-snapshot
FFTW: an adaptive software architecture for the FFT
inferredcites →https://doi.org/10.1109/ipdps.2016.119openalex-snapshot
ZNN -- A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-core and Many-Core Shared Memory Machines
inferredcites →https://doi.org/10.1109/tassp.1975.1162708openalex-snapshot
Theory and application of digital signal processing
inferredcites →https://doi.org/10.1109/tsmc.1978.4309918openalex-snapshot
Theory and Application of Digital Signal Processing
inferredcites →https://doi.org/10.1137/07070111xopenalex-snapshot
Tensor Decompositions and Applications
inferredcites →https://doi.org/10.1137/1.9781611970364openalex-snapshot
Arithmetic Complexity of Computations
inferredcites →https://doi.org/10.1137/1012065openalex-snapshot
The Art of Computer Programming. Volume 2: Seminumerical Algorithms (Donald E. Knuth)
inferredcites →https://doi.org/10.1145/2591635.2667155openalex-snapshot
Strategies for cache and local memory management by global program transformation
inferredcites →https://doi.org/10.1145/2807591.2807671openalex-snapshot
An input-adaptive and in-place approach to dense tensor-times-matrix multiply
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3079079.3079081openalex-snapshot
Compile-time optimized and statically scheduled N-D convnet primitives for multi-core and many-core (Xeon Phi) CPUs
inferredcites →https://doi.org/10.1145/3178487.3178496openalex-snapshot
Optimizing N-dimensional, winograd-based convolution for manycore CPUs
inferredcites →https://doi.org/10.2307/2317055openalex-snapshot
The Art of Computer Programming. Volume 2: Seminumerical Algorithms.
inferredcites →https://doi.org/10.48550/arxiv.1312.5851openalex-snapshot
Fast Training of Convolutional Networks through FFTs
inferredcites →https://doi.org/10.48550/arxiv.1402.1869openalex-snapshot
On the Number of Linear Regions of Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1410.0759openalex-snapshot
cuDNN: Efficient Primitives for Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1412.7580openalex-snapshot
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
inferredcites →https://doi.org/10.48550/arxiv.1611.06565openalex-snapshot
Deep Tensor Convolution on Multicores
inferredcites →https://doi.org/10.48550/arxiv.1809.07851openalex-snapshot
FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why
inferredcites →https://doi.org/10.5555/3014904.3015002openalex-snapshot
ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s11227-022-04581-6openalex-snapshot
A BLIS-like matrix multiplication for machine learning in the RISC-V ISA-based GAP8 processor
inferred← citeshttps://doi.org/10.1007/s11227-023-05050-4openalex-snapshot
Performance–energy trade-offs of deep learning convolution algorithms on ARM processors
inferred← citeshttps://doi.org/10.1007/s11227-023-05088-4openalex-snapshot
Efficient and portable Winograd convolutions for multi-core processors
inferred← citeshttps://doi.org/10.1007/s42514-025-00253-yopenalex-snapshot
Optimizing Standard Convolution for Diverse Precision on DCU
inferred← citeshttps://doi.org/10.1016/j.jpdc.2025.105108openalex-snapshot
ConCeal: A Winograd convolution code template for optimising GCU in parallel
inferred← citeshttps://doi.org/10.1016/j.sysarc.2024.103186openalex-snapshot
Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architectures
inferred← citeshttps://doi.org/10.1109/access.2024.3354720openalex-snapshot
SIMD-Constrained Lookup Table for Accelerating Variable-Weighted Convolution on x86/64 CPUs
inferred← citeshttps://doi.org/10.1109/hpec58863.2023.10363540openalex-snapshot
Towards a Flexible Hardware Implementation for Mixed-Radix Fourier Transforms
inferred← citeshttps://doi.org/10.1109/pdp55904.2022.00015openalex-snapshot
Towards Portable Realizations of Winograd-based Convolution with Vector Intrinsics and OpenMP
inferred← citeshttps://doi.org/10.1109/sbac-pad55451.2022.00027openalex-snapshot
Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor
inferred← citeshttps://doi.org/10.1109/tnnls.2021.3071762openalex-snapshot
A Survey of Deep Learning on CPUs: Opportunities and Co-Optimizations
inferred← citeshttps://doi.org/10.1109/tpds.2021.3084813openalex-snapshot
Optimizing Depthwise Separable Convolution Operations on GPUs
inferred← citeshttps://doi.org/10.1109/tpds.2023.3322907openalex-snapshot
Frequency-Domain Inference Acceleration for Convolutional Neural Networks Using ReRAMs
inferred← citeshttps://doi.org/10.1145/3342195.3387549openalex-snapshot
Accelerating winograd convolutions using symbolic computation and meta-programming
inferred← citeshttps://doi.org/10.1145/3472456.3472464openalex-snapshot
LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs
inferred← citeshttps://doi.org/10.1145/3546591.3547529openalex-snapshot
Optimizing half precision Winograd convolution on ARM many-core processors
inferred← citeshttps://doi.org/10.1145/3632956openalex-snapshot
Fast Convolution Meets Low Precision: Exploring Efficient Quantized Winograd Convolution on Modern CPUs
inferred← citeshttps://doi.org/10.2139/ssrn.7512643openalex-snapshot
Priority-Based in-GPU Scheduling with Slice for Dynamically Loaded DNN Inference Tasks
inferred← citeshttps://doi.org/10.3389/fcomp.2024.1371052openalex-snapshot
High-quality implementation for a continuous-in-time financial API in C#
inferred← citeshttps://doi.org/10.48550/arxiv.1912.02165openalex-snapshot
L3 Fusion: Fast Transformed Convolutions on CPUs
inferred← citeshttps://doi.org/10.5121/csit.2021.1117openalex-snapshot
Computer Science and Information Technology Trends
inferred← citeshttps://doi.org/10.5121/csit.2021.111716openalex-snapshot
Fast Convolution based on Winograd Minimum Filtering: Introduction and Development
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.