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Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing

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What this work says

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

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01199openalex-snapshot

AdaViT: Adaptive Vision Transformers for Efficient Image Recognition

inferredcites →https://doi.org/10.1109/cvprw56347.2022.00309openalex-snapshot

ResNeSt: Split-Attention Networks

inferredcites →https://doi.org/10.1109/hpca56546.2023.10071047openalex-snapshot

HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers

inferredcites →https://doi.org/10.1109/icassp.2007.366913openalex-snapshot

Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models

inferredcites →https://doi.org/10.1109/tc.2021.3064608openalex-snapshot

General Reuse-Centric CNN Accelerator

inferredcites →https://doi.org/10.1109/tnnls.2022.3227717openalex-snapshot

A Survey of Visual Transformers

inferredcites →https://doi.org/10.1109/tpami.2019.2926463openalex-snapshot

The ApolloScape Open Dataset for Autonomous Driving and Its Application

inferredcites →https://doi.org/10.1109/tsusc.2021.3060690openalex-snapshot

AdaPrune: An Accelerator-Aware Pruning Technique for Sustainable CNN Accelerators

inferredcites →https://doi.org/10.1109/tsusc.2023.3252039openalex-snapshot

Edge-Enhanced QoS Aware Compression Learning for Sustainable Data Stream Analytics

inferredcites →https://doi.org/10.1145/276698.276876openalex-snapshot

Approximate nearest neighbors

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1145/3233300openalex-snapshot

Low Complexity Multiply-Accumulate Units for Convolutional Neural Networks with Weight-Sharing

inferredcites →https://doi.org/10.1145/3330345.3330384openalex-snapshot

Deep reuse

inferredcites →https://doi.org/10.1145/3377930.3389816openalex-snapshot

Expected improvement versus predicted value in surrogate-based optimization

inferredcites →https://doi.org/10.1145/3447818.3460375openalex-snapshot

SumMerge

inferredcites →https://doi.org/10.1145/3503469openalex-snapshot

Energy Efficient Boosting of GEMM Accelerators for DNN via Reuse

inferredcites →https://doi.org/10.1145/3564663openalex-snapshot

Survey: Exploiting Data Redundancy for Optimization of Deep Learning

inferredcites →https://doi.org/10.1145/3566052openalex-snapshot

Occam: Optimal Data Reuse for Convolutional Neural Networks

inferredcites →https://doi.org/10.1609/aaai.v32i1.11785openalex-snapshot

Automatic Parameter Tying: A New Approach for Regularized Parameter Learning in Markov Networks

inferredcites →https://doi.org/10.3389/frobt.2015.00036openalex-snapshot

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

inferredcites →https://doi.org/10.48550/arxiv.1509.02897openalex-snapshot

Practical and Optimal LSH for Angular Distance

inferredcites →https://doi.org/10.48550/arxiv.2010.11929openalex-snapshot

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

inferredcites →https://doi.org/10.48550/arxiv.2111.15668openalex-snapshot

AdaViT: Adaptive Vision Transformers for Efficient Image Recognition

What points at this work

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

inferred← citeshttps://doi.org/10.70675/12a3f1afza163z4f13zb3cfz9a3425c3d45dopenalex-snapshot

Efficient Hardware-aware Neural Architecture Search for Edge Computing

inferred← citeshttps://doi.org/10.70675/a25bbd6cz8a5bz4d93za232z46df005062c4openalex-snapshot

Efficient Deployment of Deep Neural Networks on Hardware Devices for Edge AI

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