DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators | Paper Citation Record

Observation 9cd6359c-74e2-45c4-85eb-204e0d5cba72 · outbound

This paper cites Protean: An energy-efficient and heterogeneous platform for adaptive and hardware-accelerated battery-free computing.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Protean: An energy-efficient and heterogeneous platform for adaptive and hardware-accelerated battery-free computing

Reference 1

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Observation 748508e2-1847-4abc-ba55-d6747a90f03c · outbound

This paper cites Food-101 – mining discriminative components with random forests.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Food-101 – mining discriminative components with random forests

Reference 2

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This paper cites Large-scale machine learning with stochastic gradient descent.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Large-scale machine learning with stochastic gradient descent

Reference 3

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This paper cites Once-for-all: Train one network and specialize it for efficient deployment.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Once-for-all: Train one network and specialize it for efficient deployment

Reference 4

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This paper cites Proxylessnas: Direct neural architecture search on target task and hardware.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Proxylessnas: Direct neural architecture search on target task and hardware

Reference 5

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Observation 45ce68e8-46f1-4015-9dec-803c9e14f5fc · outbound

This paper cites Fine-grained hardware acceleration for efficient batteryless intermittent inference on the edge.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Fine-grained hardware acceleration for efficient batteryless intermittent inference on the edge

Reference 6

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Observation 2163e527-0e10-4d9e-823a-396c6a5375d6 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 7

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This paper cites https://coral.ai/products/dev-board-micro/.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://coral.ai/products/dev-board-micro/

Reference 8

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Observation 3d0d7720-f956-409a-ac06-8c4122449467 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Imagenet: A large- scale hierarchical image database

Reference 9

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Observation a45f4e72-0c63-4f2f-9933-ba42326ca16c · outbound

This paper cites Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers

Reference 10

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Observation 2d902d15-81b9-4ca7-8ef7-a7bc17badd09 · outbound

This paper cites Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

Reference 11

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://greenwaves-technologies.com/ low-power-processor/

Reference 12

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Observation 77c4ae56-3ede-42a6-8282-367440db21b9 · outbound

This paper cites Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables

Reference 13

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This paper cites Caltech-256 object category dataset.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Caltech-256 object category dataset

Reference 14

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This paper cites Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Reference 15

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This paper cites Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

Reference 16

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Observation f2afcacf-d353-4487-bcda-fd4ba7b65a0a · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Channel pruning for accelerating very deep neural networks

Reference 17

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Observation 03a3a7be-82a7-494f-9977-7e1383f1abed · outbound

This paper cites Imagenette.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Imagenette

Reference 18

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Observation 676527f0-7376-4c49-bc44-84294bf28a20 · outbound

This paper cites Ai8x synthesis repository.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Ai8x synthesis repository

Reference 19

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Observation 2184d3ec-5ebf-4e33-9430-c11aba931745 · outbound

This paper cites Ai8x training repository.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Ai8x training repository

Reference 20

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This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 21

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This paper cites Adam: A method for stochastic optimization.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Adam: A method for stochastic optimization

Reference 22

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This paper cites TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge

Reference 23

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This paper cites µnas: Constrained neural architecture search for microcontrollers.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators µnas: Constrained neural architecture search for microcontrollers

Reference 24

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This paper cites Differentiable neural network pruning to enable smart applications on microcontrollers.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Differentiable neural network pruning to enable smart applications on microcontrollers

Reference 25

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This paper cites MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

Reference 26

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Runtime neural pruning

Reference 27

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This paper cites On- device training under 256kb memory.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators On- device training under 256kb memory

Reference 28

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators An intriguing failing of convolutional neural networks and the coordconv solution

Reference 29

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness

Reference 30

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Metapruning: Meta learning for automatic neural network channel pruning

Reference 31

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Learning efficient convolutional networks through network slimming

Reference 32

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://www.analog.com/en/products/max32650.html

Reference 33

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This paper cites https://www.analog.com/en/products/max78000.html.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://www.analog.com/en/products/max78000.html

Reference 34

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This paper cites https: //cms.tinyml.org/wp-content/uploads/talks2020/tinyML_Talks_Kris_Ardis_ and_Robert_Muchsel_-201027.pdf.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https: //cms.tinyml.org/wp-content/uploads/talks2020/tinyML_Talks_Kris_Ardis_ and_Robert_Muchsel_-201027.pdf

Reference 35

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://www.analog.com/en/design-center/ evaluation-hardware-and-software/evaluation-boards-kits/max78000fthr

Reference 36

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://www.analog.com/en/products/max78002.html

Reference 37

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This paper cites https://www.analog.com/en/design-center/ evaluation-hardware-and-software/evaluation-boards-kits/max78002evkit.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://www.analog.com/en/design-center/ evaluation-hardware-and-software/evaluation-boards-kits/max78002evkit

Reference 38

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This paper cites Tinyissimoyolo: A quantized, low-memory footprint, tinyml object detection network for low power microcon- trollers.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Tinyissimoyolo: A quantized, low-memory footprint, tinyml object detection network for low power microcon- trollers

Reference 39

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Ultra-low power dnn accelerators for iot: Resource characterization of the max78000

Reference 40

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Pytorch: An imperative style, high-performance deep learning library

Reference 41

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Xnor-net: Imagenet classification using binary convolutional neural networks

Reference 42

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Kp2dtiny: Quantized neural keypoint detection and description on the edge

Reference 43

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This paper cites Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers

Reference 44

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 45

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Smith, and Oren Etzioni

Reference 46

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DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators https://www.st.com/en/microcontrollers-microprocessors/ stm32f7-series.html

Reference 47

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This paper cites Efficientnetv2: Smaller models and faster training.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Efficientnetv2: Smaller models and faster training

Reference 48

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This paper cites Haq: Hardware-aware automated quantization with mixed precision.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Haq: Hardware-aware automated quantization with mixed precision

Reference 49

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This paper cites Depth-aware cnn for rgb-d segmentation.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Depth-aware cnn for rgb-d segmentation

Reference 50

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This paper cites Location Augmentation for CNN.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Location Augmentation for CNN

Reference 51

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Observation 38c5141b-bb98-47fc-be3d-036e0a16976e · outbound

This paper cites Streamnet: Memory- efficient streaming tiny deep learning inference on the microcontroller.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Streamnet: Memory- efficient streaming tiny deep learning inference on the microcontroller

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:19.001795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 31bfe258-e341-4250-8cba-34bbf9322035 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T19:37:18.459036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34ba50dc-db55-46bd-99fe-e9b9806c8ebd · outbound

This paper cites [Yes] " is generally preferable to.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators [Yes] " is generally preferable to

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.985565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a3ff37e8-7435-4c4d-8ee5-ffe75d7817e2 · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.968141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 216ab173-ea94-47c2-a5e8-fd687ef183a7 · outbound

This paper cites Limitations.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Limitations

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.949073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f9a28a38-c122-42a5-9528-85b0d2a239af · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.929636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b1e8b3d-efd7-44bd-b1a0-5a0345c8b1b8 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not include experiments

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.911427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 934ff035-c35b-4863-bb48-199d504e5480 · outbound

This paper cites Guidelines: • The answer NA means that paper does not include experiments requiring code.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that paper does not include experiments requiring code

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.895838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 67ed210b-4a9e-4c58-ba24-c271ca4999d6 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not include experiments

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.878623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b6f5f4b-041d-49e2-ba28-0c3f92940957 · outbound

This paper cites We ran the experiments with three random seems (0,1,2) and reported the standard deviations.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators We ran the experiments with three random seems (0,1,2) and reported the standard deviations

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.862445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9692f5f-4fe8-454a-adc5-150f715c9367 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not include experiments

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.844813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 307001d1-6016-430b-b89a-ec2a68c5a9f9 · outbound

This paper cites Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T19:37:18.514005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffccc775-02d2-4571-8e16-538047360e1c · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.816708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be752fee-6af3-457d-90f2-58f45abc5f92 · outbound

This paper cites Guidelines: • The answer NA means that the paper poses no such risks.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper poses no such risks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.798658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 75f5d750-dbe8-497a-8579-91169a68c9f8 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not use existing assets

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.781389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 02fe5aaa-d3bf-4e0a-b813-1c04ee8e0618 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not release new assets.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not release new assets

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.764395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4eceef94-e294-4dfa-bbe0-e15d3aebcf91 · outbound

This paper cites 26 Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators 26 Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.747323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 480ffaa4-b67f-42f8-8ce1-399cb6297893 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:37:18.730859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers