Paper Citation Record · LEDGER

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression

As of 22 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2606.20414.

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

pith.paper-citation-record.v1
2606.20414 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T15:19:08.101009Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

37 of 37 outbound references displayed

verified exact1
verified fuzzy0
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88fd0157-0be7-416b-bf4a-a5bc8c148406 · outbound

This paper cites Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbfea3e2-16fe-49cc-b8e5-53d1d8d4e522 · outbound

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

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Going deeper with directly-trained larger spiking neural networks,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 423746e6-964b-4ffb-b179-818abdd663fc · outbound

This paper cites Temporal separation with entropy regularization for knowledge distillation in spiking neural networks,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Temporal separation with entropy regularization for knowledge distillation in spiking neural networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5d2a4c-17ce-4463-b1a2-da9f65c45a1d · outbound

This paper cites The architecture design and training optimization of spiking neural network with low-latency and high-performance for classification and segmentation,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression The architecture design and training optimization of spiking neural network with low-latency and high-performance for classification and segmentation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0b47b52-f679-4046-b836-7f11c903ca41 · outbound

This paper cites BriLLM: Brain- inspired large language model,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression BriLLM: Brain- inspired large language model,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5833b5d3-69e7-4f5f-b515-8b9c3b6aa788 · outbound

This paper cites A large scale event- based detection dataset for automotive,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression A large scale event- based detection dataset for automotive,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daa36d97-f12b-4ee4-a177-ec0af1924b74 · outbound

This paper cites Spike- driven transformer,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Spike- driven transformer,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ddd5a7-df55-4211-a99f-6cdc8eed7983 · outbound

This paper cites Spikingformer: A key foundation model for spiking neural networks,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Spikingformer: A key foundation model for spiking neural networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6350ee0b-4915-49ee-93c1-7a1172ece9a4 · outbound

This paper cites Spiking transformer with spatial-temporal attention,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Spiking transformer with spatial-temporal attention,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 609daaba-edd8-4e67-8c9b-2e00057fb9b9 · outbound

This paper cites An energy-efficient unstructured sparsity-aware deep SNN accelerator with 3-D computation array,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression An energy-efficient unstructured sparsity-aware deep SNN accelerator with 3-D computation array,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1475549a-6e14-47c2-97eb-7affa8b44924 · outbound

This paper cites MINT: Multiplier-less integer quantization for energy efficient spiking neural networks,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression MINT: Multiplier-less integer quantization for energy efficient spiking neural networks,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c10b0f6-b1d4-4a57-997a-d345c156b802 · outbound

This paper cites Phi: Leveraging pattern-based hierarchical sparsity for high-efficiency spiking neural networks,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Phi: Leveraging pattern-based hierarchical sparsity for high-efficiency spiking neural networks,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bf2aaa4-136b-4203-8401-452d4960540f · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-04T05:49:38.101391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 50f0d05c-49b6-44c7-8180-effb3d7478be · outbound

This paper cites SiBrain: A sparse spatio-temporal parallel neuromorphic architecture for accelerating spiking convolution neural networks with low latency,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression SiBrain: A sparse spatio-temporal parallel neuromorphic architecture for accelerating spiking convolution neural networks with low latency,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90d162e0-8731-417c-95ad-bb3d366fa8d2 · outbound

This paper cites Exploring the sparsity-quantization interplay on a novel hybrid SNN event-driven architecture,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Exploring the sparsity-quantization interplay on a novel hybrid SNN event-driven architecture,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bfac392-c0cf-41ad-86d6-3123afbe6a63 · outbound

This paper cites FireFly-S: Exploiting dual-side sparsity for spiking neural networks acceleration with reconfigurable spatial architecture,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression FireFly-S: Exploiting dual-side sparsity for spiking neural networks acceleration with reconfigurable spatial architecture,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2bc8ef7-d1ba-443e-ba3e-864d71237581 · outbound

This paper cites STISA: A 0.16-GOPS/W/PE single-shot inference FPGA-based SNN accelerator with algorithm and hardware co-design,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression STISA: A 0.16-GOPS/W/PE single-shot inference FPGA-based SNN accelerator with algorithm and hardware co-design,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f96ab3c6-3a7a-46bc-8143-6eba81a08014 · outbound

This paper cites FireFly-T: High- throughputsparsityexploitationforspikingtransformeraccelerationwith dual-engine overlay architecture,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression FireFly-T: High- throughputsparsityexploitationforspikingtransformeraccelerationwith dual-engine overlay architecture,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fd565dd-539a-4dd3-8930-1d29b71cb3ba · outbound

This paper cites SpikeHard: Efficiency-driven neu- romorphic hardware for heterogeneous systems-on-chip,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression SpikeHard: Efficiency-driven neu- romorphic hardware for heterogeneous systems-on-chip,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d3afdd7-7e42-4a40-9da8-4e1fae0f15eb · outbound

This paper cites NEURAL: An elastic neuromorphic architec- ture with hybrid data-event execution and on-the-fly attention dataflow,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression NEURAL: An elastic neuromorphic architec- ture with hybrid data-event execution and on-the-fly attention dataflow,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242caf11-b45c-42c3-8e98-87275bf50ebd · outbound

This paper cites Cerebron: A reconfigurable architecture for spatiotemporal sparse spiking neural networks,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Cerebron: A reconfigurable architecture for spatiotemporal sparse spiking neural networks,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fcd4d09-6343-45f0-be9f-eb37e21e8d31 · outbound

This paper cites SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e091eb1-3ce1-4b59-b743-7dbcfc6976fd · outbound

This paper cites DeepFire2: A convolutional spiking neural network accelerator on FPGAs,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression DeepFire2: A convolutional spiking neural network accelerator on FPGAs,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6b042ae-3ef9-44ae-bd5f-5231f4bfa4a6 · outbound

This paper cites Advancing neuromorphic architecture toward emerging spik- ing neural network on FPGA,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Advancing neuromorphic architecture toward emerging spik- ing neural network on FPGA,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89a8ac07-d7aa-4f59-8a9a-881477ee6b68 · outbound

This paper cites SConvNSys: Acceleratingspikingconvolutionalneuralnetworkswithareconfigurable neuromorphic architecture for diverse applications,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression SConvNSys: Acceleratingspikingconvolutionalneuralnetworkswithareconfigurable neuromorphic architecture for diverse applications,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77555490-5088-45e3-9a92-d8e23a8a3549 · outbound

This paper cites ShortcutFusion: From tensorflow to FPGA-based accelerator with a reuse-aware memory allocation for shortcut data,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression ShortcutFusion: From tensorflow to FPGA-based accelerator with a reuse-aware memory allocation for shortcut data,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea468495-19d5-4126-941f-32b4b0589add · outbound

This paper cites FPGA-NHAP: A general FPGA- based neuromorphic hardware acceleration platform with high speed and low power,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression FPGA-NHAP: A general FPGA- based neuromorphic hardware acceleration platform with high speed and low power,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c5eb0c9-b1cc-4fff-bf1c-666e1a2f490c · outbound

This paper cites An FPGA- based event-driven SNN accelerator for DVS applications with structured sparsity and early-stop,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression An FPGA- based event-driven SNN accelerator for DVS applications with structured sparsity and early-stop,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14f63a3f-25da-4503-b430-11c8fdcedb95 · outbound

This paper cites Spiker+: A framework for the generation of efficient spiking neural networks FPGA accelerators for inference at the edge,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Spiker+: A framework for the generation of efficient spiking neural networks FPGA accelerators for inference at the edge,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60145b09-75d6-4935-848c-b4171e0fbc17 · outbound

This paper cites Retina- inspired lightweight spiking convolutional neural network for single- image dehazing,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Retina- inspired lightweight spiking convolutional neural network for single- image dehazing,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66beebe5-99dd-4014-9060-79bb05aa2d13 · outbound

This paper cites The implementation and optimization of neuromorphic hardware for supporting spiking neural networks with MLP and CNN topologies,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression The implementation and optimization of neuromorphic hardware for supporting spiking neural networks with MLP and CNN topologies,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68a6b97-5242-451c-853e-1d58a800fef4 · outbound

This paper cites SyncNN: Evaluating and accel- erating spiking neural networks on FPGAs,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression SyncNN: Evaluating and accel- erating spiking neural networks on FPGAs,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2db7be35-cd64-43c4-bcd0-000e667d5b86 · outbound

This paper cites DeepFire: Acceleration of convolutional spiking neural network on modern field programmable gate arrays,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression DeepFire: Acceleration of convolutional spiking neural network on modern field programmable gate arrays,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5de5a7e8-33f6-4f33-8d56-14abdeb705ed · outbound

This paper cites FPSpike: A fully parallel and reconfigurable architecture for accelerating spiking neural networks with structured sparsity,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression FPSpike: A fully parallel and reconfigurable architecture for accelerating spiking neural networks with structured sparsity,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 973417d1-ef48-4335-b342-f3c02d126c87 · outbound

This paper cites PULSE: Parametric hardware units for low- power sparsity-aware convolution engine,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression PULSE: Parametric hardware units for low- power sparsity-aware convolution engine,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17059059-0e91-49f5-86d7-2a3065f7425e · outbound

This paper cites Low-cost deployment and acceleration of event-based spiking convolutional neural networks,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression Low-cost deployment and acceleration of event-based spiking convolutional neural networks,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30933e39-dbf6-450d-ad03-b099dd9f53c7 · outbound

This paper cites ActiveN: A scalable and flexibly-programmable event-driven neuromorphic processor,.

ExSpike: A General Full-Event Neuromorphic Architecture for Exploiting Irregular Sparsity with Event Compression ActiveN: A scalable and flexibly-programmable event-driven neuromorphic processor,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T15:19:08.101009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.