Paper Citation Record · LEDGER

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference

As of 11 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.10089.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.10089 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:33:27.158617Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 30996ece-4110-4c43-b2a6-930fe840920e · outbound

This paper cites Deep convolutional neural networks for image classification: A comprehensive review,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Deep convolutional neural networks for image classification: A comprehensive review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:29.168919Z

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Observation 6969c225-9aad-4f49-b329-b50836191846 · outbound

This paper cites A Survey of the Usages of Deep Learning for Natural Language Processing,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A Survey of the Usages of Deep Learning for Natural Language Processing,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:29.145316Z

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Observation 51221a17-f52d-411f-82b8-b14833f4a3f1 · outbound

This paper cites A review of deep learning techniques for speech processing,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A review of deep learning techniques for speech processing,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.947312Z

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Observation 6b17fa21-d79e-484b-bcee-f6a185bbd344 · outbound

This paper cites Compressing fully connected layers of deep neural networks using permuted features,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Compressing fully connected layers of deep neural networks using permuted features,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.868254Z

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Observation 7d6d5e9c-b340-4023-b87b-cc3648a66dfe · outbound

This paper cites Towards Optimal Compression: Joint Pruning and Quantization,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Towards Optimal Compression: Joint Pruning and Quantization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.852696Z

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Observation 6ca4e812-02a5-423f-bb44-bf7ff22ced9a · outbound

This paper cites Hardware-aware approach to deep neural network optimization,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Hardware-aware approach to deep neural network optimization,

Reference 6

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Observation 51a0e84a-2832-46c8-a22b-d07a81c31db9 · outbound

This paper cites Pruning and quantization for deep neural network acceleration: A survey,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Pruning and quantization for deep neural network acceleration: A survey,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.805470Z

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Observation 719113a9-16f1-4fc3-8010-6beb6bc91196 · outbound

This paper cites Knowledge Distillation: A Survey,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Knowledge Distillation: A Survey,

Reference 8

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Observation 8b100f90-1fba-4b92-b192-06a84f0316fe · outbound

This paper cites Optimizing Off- Chip Memory Access for Deep Neural Network Accelerator,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Optimizing Off- Chip Memory Access for Deep Neural Network Accelerator,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.606290Z

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Observation bbef3339-708d-4778-8119-46ef242dd771 · outbound

This paper cites Adaptation in Edge Computing: A review on design principles and research challenges,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Adaptation in Edge Computing: A review on design principles and research challenges,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.584055Z

Source-reported events for the cited work

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Observation b8154b9b-864a-456c-a3e6-82c9637fe217 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Distilling the Knowledge in a Neural Network

Reference 11

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Observation 63709127-7668-4913-9b4a-790a7efb2bb8 · outbound

This paper cites An Attentive Pruning Method for Edge Computing,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference An Attentive Pruning Method for Edge Computing,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.565537Z

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Observation 23db5362-9d34-4d1a-85af-1a6e066ebec8 · outbound

This paper cites Hardware implementation of memristor-based artificial neural networks,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Hardware implementation of memristor-based artificial neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.504173Z

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Observation ffa75126-47b5-478e-86f8-c0080da8b36b · outbound

This paper cites Effi- cient Parallel Median Filter for Image Denoising: Implementation and Performance Evaluation,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Effi- cient Parallel Median Filter for Image Denoising: Implementation and Performance Evaluation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.392190Z

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Observation b4b0f098-ed28-4645-8fd5-1d5350f83553 · outbound

This paper cites Robust and memory-less median estimation for real-time spike detection,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Robust and memory-less median estimation for real-time spike detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.375019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 72662d03-e616-4df6-9c6a-58d993f4ac9f · outbound

This paper cites An efficient switching median filter based on local outlier factor,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference An efficient switching median filter based on local outlier factor,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.358046Z

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Observation 66a798fc-cc8b-4ddf-bce5-97a6ea17f466 · outbound

This paper cites In-Memory Computing with Memristor Content Addressable Memories for Pattern Matching,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference In-Memory Computing with Memristor Content Addressable Memories for Pattern Matching,

Reference 17

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Observation 1af780ed-d848-4686-9800-17812d58656f · outbound

This paper cites Analog content-addressable memories with memristors,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Analog content-addressable memories with memristors,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.254166Z

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Observation 1134e208-834d-4c47-a194-8e712a349ea3 · outbound

This paper cites A 9t4r rram-based acam for analogue template matching at the edge,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A 9t4r rram-based acam for analogue template matching at the edge,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:28.126216Z

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Observation 8487b0eb-1b3f-47e9-a250-45f8c3dd8716 · outbound

This paper cites In-memory computing with resistive switching devices,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference In-memory computing with resistive switching devices,

Reference 20

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Observation 6afde797-75b0-42bb-aac5-12ede5aa4f5c · outbound

This paper cites Memory devices and applications for in-memory computing,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Memory devices and applications for in-memory computing,

Reference 21

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This paper cites A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation

Reference 22

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Observation 317ba2a3-8896-436c-a824-80fd91a65033 · outbound

This paper cites Tree-based machine learning performed in-memory with memristive analog CAM,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Tree-based machine learning performed in-memory with memristive analog CAM,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:27.994334Z

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Observation 40c34560-5719-4542-af7a-8a5400cfc2cb · outbound

This paper cites Differentiable Content Addressable Memory with Memristors,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Differentiable Content Addressable Memory with Memristors,

Reference 24

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Observation aadb630d-3775-4d56-9a6b-f7534b2536d5 · outbound

This paper cites Combining Multiple tinyML Models for Multimodal Context-Aware Stress Recognition on Con- strained Microcontrollers,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Combining Multiple tinyML Models for Multimodal Context-Aware Stress Recognition on Con- strained Microcontrollers,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:27.944217Z

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Observation 0179e2e6-90e9-45ca-828f-ccee854bacea · outbound

This paper cites A comprehensive technology agnostic RRAM characterisation protocol,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A comprehensive technology agnostic RRAM characterisation protocol,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:27.813052Z

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This paper cites A 1T1R+2T Ana- log Content-Addressable Memory Pixel for Online Template Matching,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A 1T1R+2T Ana- log Content-Addressable Memory Pixel for Online Template Matching,

Reference 27

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Observation 3934820f-3c8c-4f54-b465-83a95e762af7 · outbound

This paper cites Learning multiple layers of features from tiny images,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Learning multiple layers of features from tiny images,

Reference 28

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Observation 887db1d1-9edf-441d-9807-42f1e16fd462 · outbound

This paper cites Deep residual learning for image recognition,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Deep residual learning for image recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:27.530728Z

Source-reported events for the cited work

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Observation 5bd64986-d0ab-438e-8ebc-4fc448fa0063 · outbound

This paper cites Enhancing ob- ject recognition with resnet-50: an investigation of the cifar-10 dataset,.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Enhancing ob- ject recognition with resnet-50: an investigation of the cifar-10 dataset,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:27.514446Z

Source-reported events for the cited work

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Observation 006c2324-7190-4612-8d88-335e9e9ab7fd · outbound

This paper cites 1.1 Computing’s energy problem (and what we can do about it),.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference 1.1 Computing’s energy problem (and what we can do about it),

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T19:33:27.497891Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a38d2a8-ae63-4493-a5d8-569fb4e572f6 · outbound

This paper cites Towards Optimal Compression: Joint Pruning and Quantization.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference Towards Optimal Compression: Joint Pruning and Quantization

Reference 2023

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Observation e0b6ba43-d819-441e-ae3e-ffa4aa7f391f · outbound

This paper cites A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge.

A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge

Reference 2024

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

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