Paper Citation Record · LEDGER

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 3 inbound Pith citation observations for arXiv:2502.04524.

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

pith.paper-citation-record.v1
2502.04524 v4

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:32:54.707093Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:27:40.503413Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T12:52:17.832425Z

Reference resolution

44 of 44 outbound references displayed

verified exact24
verified fuzzy4
unresolved10
parse uncertain0
malformed identifier6
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 279ebb8d-7d43-4f00-b7fe-7315b4c06a16 · outbound

This paper cites Accessed: 2024-12-06 (2024).

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Accessed: 2024-12-06 (2024)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:32:58.715162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8d63bdfc-dd75-4f1e-b0b8-84a752065d6e · outbound

This paper cites ACM SIGARCH Computer Architecture News45(2017) https://doi.org/10.1145/3140659.3080246.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices ACM SIGARCH Computer Architecture News45(2017) https://doi.org/10.1145/3140659.3080246

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.520118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3368804d-f3f0-4813-a5d5-383adb5ce8b6 · outbound

This paper cites https://doi.org/10.1109/JPROC.2017.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices https://doi.org/10.1109/JPROC.2017

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.525013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 369d0b95-e27e-4a85-860b-9f9c1abbab8b · outbound

This paper cites Proceedings of the IEEE107(2019) https://doi.org/ 10.1109/JPROC.2018.2871057.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Proceedings of the IEEE107(2019) https://doi.org/ 10.1109/JPROC.2018.2871057

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.529842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1557e66-1647-4c83-9dea-983d14b1639c · outbound

This paper cites https://doi.org/10.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices https://doi.org/10

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:32:58.701430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dff0f0ff-d595-4e1a-b7d4-c3438e542978 · outbound

This paper cites Microprocessors and Microsystems67(2019) https://doi.org/10.1016/j.micpro.2019.01.009.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Microprocessors and Microsystems67(2019) https://doi.org/10.1016/j.micpro.2019.01.009

Reference 6

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.913948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a1f1d412-27e0-4f97-8e7f-d4de2775e73b · outbound

This paper cites In: Proceedings - IEEE International Symposium on Circuits and Systems, vol.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: Proceedings - IEEE International Symposium on Circuits and Systems, vol

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.543583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab4f59f-ef5f-453d-9164-df3bfd84e7f3 · outbound

This paper cites https://doi.org/10.1080/23746149.2016.1259585.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices https://doi.org/10.1080/23746149.2016.1259585

Reference 8

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:58.390211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 30532f4b-2246-4aec-a5af-99bf44243f7c · outbound

This paper cites Nature 608(7923), 504–512 (2022) https://doi.org/10.1038/s41586-022-04992-8.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature 608(7923), 504–512 (2022) https://doi.org/10.1038/s41586-022-04992-8

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.552065Z

Source-reported events for the cited work

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Observation 9ac7fe51-6fbe-4f22-b10e-29dded4a02e0 · outbound

This paper cites Nature 577(2020) https://doi.org/10.1038/s41586-020-1942-4.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature 577(2020) https://doi.org/10.1038/s41586-020-1942-4

Reference 10

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.890946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af628b59-927d-48dd-ba30-569a4e24024e · outbound

This paper cites Nature620(2023) https://doi.org/10.1038/ s41586-023-06337-5.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature620(2023) https://doi.org/10.1038/ s41586-023-06337-5

Reference 11

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T22:32:58.687699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 05f3f55f-11ff-4843-b34e-28d1d6436b1e · outbound

This paper cites Nature Electronics6(2023) https://doi.org/10.1038/s41928-023-01010-1.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature Electronics6(2023) https://doi.org/10.1038/s41928-023-01010-1

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.565065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 798d2b08-1afd-4bd3-8a7a-220bfb8f4482 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Gemini: A Family of Highly Capable Multimodal Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.569423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6489a991-5768-40fb-aa3a-167426fdd057 · outbound

This paper cites IEEE Nanotechnology Magazine12(2018) https: //doi.org/10.1109/MNANO.2018.2844902.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices IEEE Nanotechnology Magazine12(2018) https: //doi.org/10.1109/MNANO.2018.2844902

Reference 14

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:58.186007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ee8d642-108c-4477-880b-71dbc6dba2e9 · outbound

This paper cites IEEE Transactions on Electron Devices67(2020) https://doi.org/10.1109/TED.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices IEEE Transactions on Electron Devices67(2020) https://doi.org/10.1109/TED

Reference 15

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-08T22:32:58.018846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5bea06d9-f69d-4320-abeb-60fc98acb4aa · outbound

This paper cites https://doi.org/10.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices https://doi.org/10

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:32:58.664174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 17ccb36e-54e5-4398-abf2-8354378204b9 · outbound

This paper cites In: Technical Digest - International Electron Devices Meeting, IEDM, vol.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: Technical Digest - International Electron Devices Meeting, IEDM, vol

Reference 17

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:57.863685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d73ca1b6-e527-43e1-8698-84709bc4e512 · outbound

This paper cites In: Digest of Technical Papers - Symposium on VLSI Technology, vol.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: Digest of Technical Papers - Symposium on VLSI Technology, vol

Reference 18

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:57.706838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2928b796-b405-4af0-893d-1fb427c34acf · outbound

This paper cites https://doi.org/10.1088/0268-1242/31/6/063002.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices https://doi.org/10.1088/0268-1242/31/6/063002

Reference 19

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.876881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 811c035c-7786-434b-b54a-340c7d91d38c · outbound

This paper cites Frontiers in Neuroscience10 (2016) https://doi.org/10.3389/fnins.2016.00333.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Frontiers in Neuroscience10 (2016) https://doi.org/10.3389/fnins.2016.00333

Reference 20

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:57.514238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b57dc65e-498a-4189-bd51-7493fb7793a4 · outbound

This paper cites Frontiers in Neuroscience14(2020) https://doi.org/10.3389/fnins.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Frontiers in Neuroscience14(2020) https://doi.org/10.3389/fnins

Reference 21

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-08T22:32:57.324461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 252b8653-ad4b-4846-8139-c2027bd72219 · outbound

This paper cites In: Tech- nical Digest - International Electron Devices Meeting, IEDM, vol.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: Tech- nical Digest - International Electron Devices Meeting, IEDM, vol

Reference 22

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:57.136745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 759a2983-6631-4e9f-8c7e-10d50c97d733 · outbound

This paper cites Nature Communications15(1), 7133 (2024) https://doi.org/10.1038/s41467-024-51221-z.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature Communications15(1), 7133 (2024) https://doi.org/10.1038/s41467-024-51221-z

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.611205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7346b8ff-6716-4902-90f6-ba092f51ef6e · outbound

This paper cites Nano Letters24(2024) https://doi.org/10.1021/acs.nanolett.3c03697.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nano Letters24(2024) https://doi.org/10.1021/acs.nanolett.3c03697

Reference 24

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.853435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 73bea802-3428-4aa6-81bc-c46453e87eaa · outbound

This paper cites In: 2024 Device Research Conference (DRC), pp.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: 2024 Device Research Conference (DRC), pp

Reference 25

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:56.969566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation be37813d-b114-4106-bb46-7fa4d42667ac · outbound

This paper cites Nanoscale Horiz.9, 775–784 (2024) https://doi.org/10.1039/D4NH00072B.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nanoscale Horiz.9, 775–784 (2024) https://doi.org/10.1039/D4NH00072B

Reference 26

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.838992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8467442f-3047-48ab-8fec-46b047777622 · outbound

This paper cites IEEE Transactions on Electron Devices62(2015) https://doi.org/10.1109/TED.2015.2418114.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices IEEE Transactions on Electron Devices62(2015) https://doi.org/10.1109/TED.2015.2418114

Reference 27

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:56.766742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation feb27cff-d062-4ef3-bc09-d5b498807a57 · outbound

This paper cites Journal of Physics and Chemistry of Solids5(1958) https://doi.org/ 10.1016/0022-3697(58)90069-6.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Journal of Physics and Chemistry of Solids5(1958) https://doi.org/ 10.1016/0022-3697(58)90069-6

Reference 28

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.824230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0eee4386-44da-4437-9246-fc97bcf85f2c · outbound

This paper cites 2012 4th IEEE International Memory Workshop, IMW 2012, 1–4 (2012) https://doi.org/ 10.1109/IMW.2012.6213667.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices 2012 4th IEEE International Memory Workshop, IMW 2012, 1–4 (2012) https://doi.org/ 10.1109/IMW.2012.6213667

Reference 29

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:56.555797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 628f76f1-2fc8-4315-83ae-9641dbbddd84 · outbound

This paper cites IEEE Electron Device Letters34, 680–82 (2013) https://doi.org/10.1109/LED.2013.2251602.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices IEEE Electron Device Letters34, 680–82 (2013) https://doi.org/10.1109/LED.2013.2251602

Reference 30

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:56.387879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 345fff68-3cb3-4e98-99d8-99b576b86284 · outbound

This paper cites In: 2023 IEEE International Memory Work- shop, IMW 2023 - Proceedings (2023).

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: 2023 IEEE International Memory Work- shop, IMW 2023 - Proceedings (2023)

Reference 31

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:56.208467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 106b484d-aa29-4c52-8392-d90d389b1617 · outbound

This paper cites In: 2024 IEEE European Solid-State Electronics Research Conference (ESSERC), pp.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: 2024 IEEE European Solid-State Electronics Research Conference (ESSERC), pp

Reference 32

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-08T22:32:56.005389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 903ef126-8fe2-4df4-bf4d-1505c1596bff · outbound

This paper cites Advances in Physics70(2021) https: //doi.org/10.1080/00018732.2022.2084006.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Advances in Physics70(2021) https: //doi.org/10.1080/00018732.2022.2084006

Reference 33

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:55.833776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2634c5c4-b76a-45a7-bb3e-47b94c64c1e7 · outbound

This paper cites Nature Communications11(2020) https://doi.org/10.1038/s41467-020-16108-9.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature Communications11(2020) https://doi.org/10.1038/s41467-020-16108-9

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.659776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 788e359f-1151-46e1-be81-24bb380a3377 · outbound

This paper cites In: Digest of Technical Papers - Symposium on VLSI Technology, vol.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: Digest of Technical Papers - Symposium on VLSI Technology, vol

Reference 35

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:55.640253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bc2ebe5a-3219-4590-a6cd-8eab78b35662 · outbound

This paper cites IEEE Transactions on Electron Devices65(2018) https://doi.org/10.1109/TED.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices IEEE Transactions on Electron Devices65(2018) https://doi.org/10.1109/TED

Reference 36

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-08T22:32:55.440544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e279fc4d-0e30-4f9f-93a1-1b90d3307c44 · outbound

This paper cites In: Technical Digest - International Electron Devices Meeting, IEDM (2018).

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: Technical Digest - International Electron Devices Meeting, IEDM (2018)

Reference 37

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.798714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a299b471-d347-4e78-bc94-ddba005ff2d7 · outbound

This paper cites Nature Communications14(2023) https://doi.org/10.1038/s41467-023-41958-4.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nature Communications14(2023) https://doi.org/10.1038/s41467-023-41958-4

Reference 39

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.783573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 14c1b58b-26de-4d8f-81c5-951427d01228 · outbound

This paper cites Scientific Reports 8(1), 7178 (2018) https://doi.org/10.1038/s41598-018-25376-x.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Scientific Reports 8(1), 7178 (2018) https://doi.org/10.1038/s41598-018-25376-x

Reference 40

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.768506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4414c1c5-5fb3-4cdd-b522-73197197a0c9 · outbound

This paper cites Scientific Reports13 (2023) https://doi.org/10.1038/s41598-023-42214-x.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Scientific Reports13 (2023) https://doi.org/10.1038/s41598-023-42214-x

Reference 41

Resolution: verified exact; doi, observed 2026-08-08T22:32:54.753359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4678af4c-7d27-44a3-9b9e-8be016effa08 · outbound

This paper cites Oxford at the Clarendon Press, 2 ed.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Oxford at the Clarendon Press, 2 ed

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:32:58.650057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ecd28108-4b33-4f1c-806d-ccda2dde27f4 · outbound

This paper cites Nanotechnology 23(2012) https://doi.org/10.1088/0957-4484/23/7/075201.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Nanotechnology 23(2012) https://doi.org/10.1088/0957-4484/23/7/075201

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T22:32:54.698498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aba983b3-3e7e-4d34-8002-dbf4e22b05cd · outbound

This paper cites In: 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp

Reference 44

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:32:55.265336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation deeda867-f6a8-4fe8-9393-b020914a25a4 · outbound

This paper cites In: 2019 26th IEEE International Conference on Electron- ics, Circuits and Systems (ICECS), pp.

All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices In: 2019 26th IEEE International Conference on Electron- ics, Circuits and Systems (ICECS), pp

Reference 45

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-08T22:32:55.094335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 34b2f25c-bb80-4d84-a843-03aa0d0702aa · inbound

PdNeuRAM: forming-free, multi-bit Pd/HfO2 ReRAM for energy-efficient neuromorphic computing cites this paper.

PdNeuRAM: forming-free, multi-bit Pd/HfO2 ReRAM for energy-efficient neuromorphic computing All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T12:52:17.833933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 842e0d4f-5600-4e34-9d80-f68c671acfe0 · inbound

Decoupling Electric Field and Temperature-Driven Atomistic Forming Mechanisms in TaOx/HfO2-Based ReRAMs using Reactive Molecular Dynamics Simulations cites this paper.

Decoupling Electric Field and Temperature-Driven Atomistic Forming Mechanisms in TaOx/HfO2-Based ReRAMs using Reactive Molecular Dynamics Simulations All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:27:40.503413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b8191f0-270e-4deb-9c5e-4c1005d997ea · inbound

Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs cites this paper.

Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T11:40:43.866640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.