Paper Citation Record · LEDGER

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.12221.

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

pith.paper-citation-record.v1
2505.12221 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:45:10.510815Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

41 of 41 outbound references displayed

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verified fuzzy29
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25bb73ba-05d4-4dc8-b952-b1810cabdd0a · outbound

This paper cites Deep learning,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Deep learning,

Reference 1

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Unavailable: canonical work link unavailable.

Observation c4035b69-c120-4b5e-afa7-853310470bde · outbound

This paper cites The growing energy footprint of artificial intelligence,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware The growing energy footprint of artificial intelligence,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:10.363616Z

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Unavailable: canonical work link unavailable.

Observation 348d0557-5ef6-4a61-9f4b-757d576b5bcd · outbound

This paper cites Towards spike-based machine intelligence with neuromorphic computing,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Towards spike-based machine intelligence with neuromorphic computing,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:10.368069Z

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Observation 4abaaf0c-b8bc-4a11-bf95-d00a1ea10670 · outbound

This paper cites A million spiking-neuron integrated circuit with a scalable communication network and interface,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware A million spiking-neuron integrated circuit with a scalable communication network and interface,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.932432Z

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Observation fb71257b-e44d-4a0a-835f-c17c8b47dbe6 · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 5

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Observation 9b0967fc-7630-4c03-9a0f-d6ebf3c466de · outbound

This paper cites Darwin3: a large-scale neuromorphic chip with a novel isa and on-chip learning,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Darwin3: a large-scale neuromorphic chip with a novel isa and on-chip learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.914517Z

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Observation 72b8b710-3aaa-4209-8634-56692fa3c358 · outbound

This paper cites Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:10.384721Z

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Observation 36d37d15-345a-4197-806e-2c36cea30b9f · outbound

This paper cites Programming spiking neural networks on intel’s loihi,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Programming spiking neural networks on intel’s loihi,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.903431Z

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Observation f78bba45-5a41-4558-b975-7b838cf16de7 · outbound

This paper cites Attention is all you need,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Attention is all you need,

Reference 9

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Observation ec164b47-6882-4fd7-9225-b0120414f6ca · outbound

This paper cites Con- version of continuous-valued deep networks to efficient event-driven networks for image classification,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Con- version of continuous-valued deep networks to efficient event-driven networks for image classification,

Reference 10

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Source-reported events for the cited work

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Observation 26901db6-3545-4af8-9cc3-302e85f453f3 · outbound

This paper cites Going deeper in spiking neural networks: Vgg and residual architectures,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Going deeper in spiking neural networks: Vgg and residual architectures,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:10.400076Z

Source-reported events for the cited work

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Observation ee1d8992-f548-4c16-8cab-38f0b01d92ad · outbound

This paper cites Spiking-yolo: spiking neural network for energy-efficient object detection,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Spiking-yolo: spiking neural network for energy-efficient object detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.872834Z

Source-reported events for the cited work

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Observation b30c3811-0b10-4ff0-9ed2-d57ff0bf1930 · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Spiking deep convolutional neural networks for energy-efficient object recognition,

Reference 13

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Source-reported events for the cited work

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Observation 5691aead-416e-4675-b915-9b9560ac9ec1 · outbound

This paper cites Fast- classifying, high-accuracy spiking deep networks through weight and threshold balancing,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Fast- classifying, high-accuracy spiking deep networks through weight and threshold balancing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.855069Z

Source-reported events for the cited work

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Observation 5d354bee-0a85-4a9e-9fc0-94c7bfca59ba · outbound

This paper cites Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.844590Z

Source-reported events for the cited work

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Observation 6db3da46-0dba-4f11-97fa-55bb1a9b7bf8 · outbound

This paper cites Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Reference 16

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Source-reported events for the cited work

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Observation 64277798-5065-4235-98f6-56e068c20a01 · outbound

This paper cites Error-aware conversion from ann to snn via post-training parameter calibration,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Error-aware conversion from ann to snn via post-training parameter calibration,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.834015Z

Source-reported events for the cited work

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Observation 676023ef-3e22-4230-b5af-08b4e17e1d54 · outbound

This paper cites Brain-inspired multi- layer perceptron with spiking neurons,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Brain-inspired multi- layer perceptron with spiking neurons,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.823275Z

Source-reported events for the cited work

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Observation 0fa3c57b-4867-4293-bfe6-6af58d11cbb2 · outbound

This paper cites Fast-snn: fast spiking neural network by converting quantized ann,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Fast-snn: fast spiking neural network by converting quantized ann,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.812879Z

Source-reported events for the cited work

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Observation ae02f59b-8e50-4110-a624-e2fdb6649471 · outbound

This paper cites Spatio-temporal backpropa- gation for training high-performance spiking neural networks,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Spatio-temporal backpropa- gation for training high-performance spiking neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.802235Z

Source-reported events for the cited work

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Observation b85b3ed3-674b-46fc-8366-f6fcc1f9f15c · outbound

This paper cites Direct training for spiking neural networks: Faster, larger, better,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Direct training for spiking neural networks: Faster, larger, better,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.791596Z

Source-reported events for the cited work

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Observation dd896f1e-4d85-4d42-9f75-9f055268c42a · outbound

This paper cites Going deeper with directly-trained larger spiking neural networks,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Going deeper with directly-trained larger spiking neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.781230Z

Source-reported events for the cited work

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Observation 3325ed97-be5d-49a8-b742-2f7ceaab353f · outbound

This paper cites Multi-level firing with spiking ds-resnet: Enabling better and deeper directly-trained spiking neural networks,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Multi-level firing with spiking ds-resnet: Enabling better and deeper directly-trained spiking neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.770510Z

Source-reported events for the cited work

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Observation 7fa29410-44c7-4ba8-a6cc-2a82674ffa08 · outbound

This paper cites Towards memory-and time-efficient backpropagation for training spiking neural networks,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Towards memory-and time-efficient backpropagation for training spiking neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.759159Z

Source-reported events for the cited work

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Observation e5cdb96f-64be-45e7-80a1-0724f696b3d0 · outbound

This paper cites An efficient spiking neural network for recognizing gestures with a dvs camera on the loihi neuromorphic processor,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware An efficient spiking neural network for recognizing gestures with a dvs camera on the loihi neuromorphic processor,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.748631Z

Source-reported events for the cited work

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Observation 8a24a15c-e2b9-453d-bb25-c020c22a9a06 · outbound

This paper cites In-hardware learning of multilayer spiking neural networks on a neuromorphic processor,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware In-hardware learning of multilayer spiking neural networks on a neuromorphic processor,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.737435Z

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 92cb5267-a43b-4935-ac14-22bc10881305 · outbound

This paper cites The backpropagation algorithm implemented on spiking neuromorphic hard- ware,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware The backpropagation algorithm implemented on spiking neuromorphic hard- ware,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.725532Z

Source-reported events for the cited work

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Observation af1654db-b895-4fe5-80d2-eb0a7cdbab1e · outbound

This paper cites A 28-nm convolutional neuromor- phic processor enabling online learning with spike-based retinas,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware A 28-nm convolutional neuromor- phic processor enabling online learning with spike-based retinas,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.714374Z

Source-reported events for the cited work

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Observation f1ce7758-53d0-43ef-ad16-53b2aaae76da · outbound

This paper cites Fast and energy-efficient neuromorphic deep learning with first-spike times,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Fast and energy-efficient neuromorphic deep learning with first-spike times,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.702865Z

Source-reported events for the cited work

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Observation 72bfdeef-dbd6-4e70-a511-0ca3e925a9bd · outbound

This paper cites hxtorch. snn: Machine-learning-inspired spiking neural network modeling on brainscales-2,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware hxtorch. snn: Machine-learning-inspired spiking neural network modeling on brainscales-2,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.690477Z

Source-reported events for the cited work

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Observation 695a4252-7489-4703-8c1d-feda0b1a0c20 · outbound

This paper cites Efficient algorithms for accelerating spiking neural networks on mac array of spinnaker 2,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Efficient algorithms for accelerating spiking neural networks on mac array of spinnaker 2,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.678528Z

Source-reported events for the cited work

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Observation 75192cbc-dcef-48b7-8032-c16ad44df07b · outbound

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

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 32

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Source-reported events for the cited work

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Observation ca0cbfa4-2ca7-4c39-ae39-e6b1de344d03 · outbound

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

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware 1.1 computing’s energy problem (and what we can do about it),

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.659125Z

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Observation a8c930cd-c0de-40e2-bec6-c373e5d724c4 · outbound

This paper cites Spikeconverter: An efficient conversion framework zipping the gap between artificial neural networks and spiking neural networks,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Spikeconverter: An efficient conversion framework zipping the gap between artificial neural networks and spiking neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.647772Z

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Observation eb6ea24d-f4fd-4ae3-99ab-6e0162b96b07 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Pytorch: An imperative style, high- performance deep learning library,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.635635Z

Source-reported events for the cited work

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Observation ea1b46a4-b056-4561-8b8e-f096232a29d0 · outbound

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

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Learning multiple layers of features from tiny images,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.622558Z

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 4609f06c-702d-4516-8df3-e6d26bc47d2b · outbound

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

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Imagenet: A large-scale hierarchical image database,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.609928Z

Source-reported events for the cited work

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Observation 7e9368d0-081b-4aa6-8b02-e614105694f3 · outbound

This paper cites The pascal visual object classes homepage,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware The pascal visual object classes homepage,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.598487Z

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 5ad234fc-748b-49bd-a480-4097ac67a214 · outbound

This paper cites You only look once: Unified, real-time object detection,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware You only look once: Unified, real-time object detection,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.586915Z

Source-reported events for the cited work

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Observation 1ea1ddc6-0852-4d30-8bb1-09cc970bfdf9 · outbound

This paper cites Constructing deep spiking neural networks from artificial neural networks with knowledge distillation,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Constructing deep spiking neural networks from artificial neural networks with knowledge distillation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:45:10.575061Z

Source-reported events for the cited work

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Observation e3cab071-dd9d-4b60-9b40-7b5db1d1346c · outbound

This paper cites Incorporating learnable membrane time constant to enhance learning of spiking neural networks,.

Bridging Quantized Artificial Neural Networks and Neuromorphic Hardware Incorporating learnable membrane time constant to enhance learning of spiking neural networks,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:45:10.510815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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