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On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-11179-7_36openalex-snapshot
Minimizing Computation in Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/asap.2009.25openalex-snapshot
A Massively Parallel Coprocessor for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot
Multi-column deep neural networks for image classification
inferredcites →https://doi.org/10.1109/cvpr.2014.214openalex-snapshot
DeepPose: Human Pose Estimation via Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2014.220openalex-snapshot
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
inferredcites →https://doi.org/10.1109/cvpr.2014.223openalex-snapshot
Large-Scale Video Classification with Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298878openalex-snapshot
Long-term recurrent convolutional networks for visual recognition and description
inferredcites →https://doi.org/10.1109/cvprw.2014.106openalex-snapshot
A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks
inferredcites →https://doi.org/10.1109/fpl.2009.5272559openalex-snapshot
CNP: An FPGA-based processor for Convolutional Networks
inferredcites →https://doi.org/10.1109/fpt.2016.7929192openalex-snapshot
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC
inferredcites →https://doi.org/10.1109/fpt.2016.7929552openalex-snapshot
A memory-based realization of a binarized deep convolutional neural network
inferredcites →https://doi.org/10.1109/hpec.2014.7040963openalex-snapshot
Memory access optimized routing scheme for deep networks on a mobile coprocessor
inferredcites →https://doi.org/10.1109/iccd.2013.6657019openalex-snapshot
Memory-centric accelerator design for Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/iscas.2010.5537908openalex-snapshot
Hardware accelerated convolutional neural networks for synthetic vision systems
inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-snapshot
3D Convolutional Neural Networks for Human Action Recognition
inferredcites →https://doi.org/10.1145/1815961.1815993openalex-snapshot
A dynamically configurable coprocessor for convolutional neural networks
inferredcites →https://doi.org/10.1145/2684746.2689060openalex-snapshot
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1145/2847263.2847265openalex-snapshot
Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
inferredcites →https://doi.org/10.1145/3020078.3021741openalex-snapshot
Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs
inferredcites →https://doi.org/10.1145/3020078.3021744openalex-snapshot
FINN
inferredcites →https://doi.org/10.1145/3020078.3021782openalex-snapshot
A Batch Normalization Free Binarized Convolutional Deep Neural Network on an FPGA (Abstract Only)
inferredcites →https://doi.org/10.1145/3020078.3021786openalex-snapshot
A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks (Abstract Only)
inferredcites →https://doi.org/10.48550/arxiv.1312.6082openalex-snapshot
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional 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.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1602.02830openalex-snapshot
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

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

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Adaptation of Convolution and Batch Normalization Layer for CNN Implementation on FPGA
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RNA: An Accurate Residual Network Accelerator for Quantized and Reconstructed Deep Neural Networks
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An object detector based on multiscale sliding window search using a fully pipelined binarized CNN on an FPGA
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Approximate computing for embedded machine learning
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