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On Multiplication of Polynomials Modulo a Polynomial

https://doi.org/10.1137/0209021
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf01683270openalex-snapshot
Some bilinear forms whose multiplicative complexity depends on the field of constants
inferredcites →https://doi.org/10.1007/bf02165411openalex-snapshot
Gaussian elimination is not optimal
inferredcites →https://doi.org/10.1073/pnas.73.4.1005openalex-snapshot
On computing the Discrete Fourier Transform
inferredcites →https://doi.org/10.1145/322003.322014openalex-snapshot
Algebras Having Linear Multiplicative Complexities

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Integration — What do we want from the theory?
inferred← citeshttps://doi.org/10.1007/978-3-319-68612-7_69openalex-snapshot
Winograd Algorithm for 3D Convolution Neural Networks
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Lesser Known FFT Algorithms
inferred← citeshttps://doi.org/10.1016/0001-8708(79)90037-9openalex-snapshot
On the multiplicative complexity of the Discrete Fourier Transform
inferred← citeshttps://doi.org/10.1088/1742-6596/1026/1/012019openalex-snapshot
A High-efficiency FPGA-based Accelerator for Convolutional Neural Networks using Winograd Algorithm
inferred← citeshttps://doi.org/10.1109/cvpr.2016.435openalex-snapshot
Fast Algorithms for Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/cvpr.2018.00349openalex-snapshot
Perturbative Neural Networks
inferred← citeshttps://doi.org/10.1109/icassp.1985.1168331openalex-snapshot
Multiply/Add tradeoffs in length-2>sup /sup
inferred← citeshttps://doi.org/10.1109/icicm59499.2023.10365973openalex-snapshot
An Efficient Accelerator on FPGA for Large Convolution and Correlation using Winograd
inferred← citeshttps://doi.org/10.1109/tassp.1986.1164785openalex-snapshot
On the number of multiplications necessary to compute a length-2nDFT
inferred← citeshttps://doi.org/10.1109/tcad.2019.2912894openalex-snapshot
Toward an Efficient Deep Pipelined Template-Based Architecture for Accelerating the Entire 2-D and 3-D CNNs on FPGA
inferred← citeshttps://doi.org/10.1109/tsp.2024.3405571openalex-snapshot
Subband-Split Co-Prime Spectral Analyzer With Convolution Window Prototype Filters
inferred← citeshttps://doi.org/10.1145/3174243.3174257openalex-snapshot
Towards a Uniform Template-based Architecture for Accelerating 2D and 3D CNNs on FPGA
inferred← citeshttps://doi.org/10.1145/3289602.3293935openalex-snapshot
Accelerating 3D CNN-based Lung Nodule Segmentation on a Multi-FPGA System
inferred← citeshttps://doi.org/10.1145/3316781.3317906openalex-snapshot
Scale-out Acceleration for 3D CNN-based Lung Nodule Segmentation on a Multi-FPGA System
inferred← citeshttps://doi.org/10.1145/3432816openalex-snapshot
Toward Multi-FPGA Acceleration of the Neural Networks
inferred← citeshttps://doi.org/10.1147/rd.264.0424openalex-snapshot
Rectangular Transforms for Digital Convolution on the Research Signal Processor
inferred← citeshttps://doi.org/10.1184/r1/6724106openalex-snapshot
Unconstrained Periocular Face Recognition: From Reconstructive Dictionary Learning to Generative Deep Learning and Beyond
inferred← citeshttps://doi.org/10.3390/e27050506openalex-snapshot
Karatsuba Algorithm Revisited for 2D Convolution Computation Optimization
inferred← citeshttps://doi.org/10.48550/arxiv.1509.09308openalex-snapshot
Fast Algorithms for Convolutional Neural Networks
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