Paper Citation Record · LEDGER

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers

As of 18 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 2 inbound Pith citation observations for arXiv:2412.05540.

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

pith.paper-citation-record.v1
2412.05540 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:41:21.642571Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:43:25.901211Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:51:38.997059Z

Reference resolution

26 of 26 outbound references displayed

verified exact2
verified fuzzy16
unresolved8
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc710e34-59b4-4380-ab50-e3dab07a61f4 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.490482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b5cca8-1401-4f7e-b55f-14dfc5164697 · outbound

This paper cites Zero-shot text-to-image generation,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Zero-shot text-to-image generation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.264997Z

Source-reported events for the cited work

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Observation a07b0ed2-e6b8-4132-8281-57948c57aafd · outbound

This paper cites Mistral 7B.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Mistral 7B

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.502618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01231065-4c37-4995-a696-df65663d7198 · outbound

This paper cites Unified scaling laws for routed language models,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Unified scaling laws for routed language models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.250930Z

Source-reported events for the cited work

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Observation 630c04d3-6f97-4d36-8d79-ef099dfdd46c · outbound

This paper cites Scaling vision with sparse mixture of experts,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Scaling vision with sparse mixture of experts,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.519139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0469497-c796-44f9-9bbb-8f807b467062 · outbound

This paper cites M3vit: Mixture-of-experts vision transformer for efficient multi- task learning with model-accelerator co-design,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers M3vit: Mixture-of-experts vision transformer for efficient multi- task learning with model-accelerator co-design,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.226290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1dd75d63-800d-4c3b-a0e0-d562a164bcb6 · outbound

This paper cites Networks of spiking neurons: The third generation of neural network models,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Networks of spiking neurons: The third generation of neural network models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.207769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d7eabd5c-0990-4a42-af51-2e010b8588c7 · outbound

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

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Towards spike-based machine intel- ligence with neuromorphic computing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.192790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation abc41cbd-dc55-4c6e-bd83-07b1e49cbee6 · outbound

This paper cites Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.179121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bcc1ecce-cd5d-4e50-aed4-85d9e761055c · outbound

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

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.552958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f0bb142-1c41-4a8b-829e-7f5da7b0fb41 · outbound

This paper cites Spikformer: When spiking neural network meets transformer,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Spikformer: When spiking neural network meets transformer,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.137091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d3faa5c3-17da-4d16-a6eb-cf0ed2d29e29 · outbound

This paper cites Spiking transformers for event-based single object tracking,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Spiking transformers for event-based single object tracking,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.113630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c2a553b6-e50d-42ab-a6b9-3769f2de6c90 · outbound

This paper cites Spikegpt: Generative pre-trained language model with spiking neural networks,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Spikegpt: Generative pre-trained language model with spiking neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.093632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 77e862d1-3657-45d9-b92b-356ddae6465d · outbound

This paper cites Spike-driven Transformer.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Spike-driven Transformer

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:41:21.933526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c7930d23-617e-46df-8b11-b783b97b05eb · outbound

This paper cites Reconfigurable dataflow optimization for spatiotem- poral spiking neural computation on systolic array accelerators,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Reconfigurable dataflow optimization for spatiotem- poral spiking neural computation on systolic array accelerators,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.073022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 734d311b-43f5-47c4-97cb-0b1646618ea7 · outbound

This paper cites Parallel time batching: Systolic- array acceleration of sparse spiking neural computation,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Parallel time batching: Systolic- array acceleration of sparse spiking neural computation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.153553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f117f8ed-ade5-48d3-86c0-bc289fc712bc · outbound

This paper cites LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:41:21.910319Z

Source-reported events for the cited work

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Observation 0496b4a5-050a-4289-a350-2dc3a3a3c674 · outbound

This paper cites Spinalflow: An architecture and dataflow tailored for spiking neural networks,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Spinalflow: An architecture and dataflow tailored for spiking neural networks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.598783Z

Source-reported events for the cited work

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Observation 35c969a9-2f04-4a0a-a1b3-a4f7b879adf0 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.604890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df4b7f45-241f-45f0-bb8d-3950f1efd3ad · outbound

This paper cites 3d-carbon: An analytical carbon modeling tool for 3d and 2.5 d integrated circuits,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers 3d-carbon: An analytical carbon modeling tool for 3d and 2.5 d integrated circuits,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.034996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7031a519-f29e-4358-94ba-36e100f94e80 · outbound

This paper cites Nebula: A neuromorphic spin-based ultra-low power architecture for snns and anns,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Nebula: A neuromorphic spin-based ultra-low power architecture for snns and anns,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:22.014632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 13cef4d3-103b-4b89-a464-67624381c2e9 · outbound

This paper cites 30.2 a 22nm 0.26 nw/synapse spike-driven spiking neural network processing unit using time-step-first dataflow and sparsity-adaptive in-memory computing,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers 30.2 a 22nm 0.26 nw/synapse spike-driven spiking neural network processing unit using time-step-first dataflow and sparsity-adaptive in-memory computing,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:21.995628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 55057ff2-f72a-4ab2-99c3-d6b7bc894bb0 · outbound

This paper cites Design and architectural co-optimization of monolithic 3d liquid state machine-based neuromorphic processor,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Design and architectural co-optimization of monolithic 3d liquid state machine-based neuromorphic processor,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.626932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20363ebb-9580-46ff-af03-082a1fca549a · outbound

This paper cites Area-efficient and low-power face-to-face-bonded 3d liquid state machine design,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Area-efficient and low-power face-to-face-bonded 3d liquid state machine design,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:21.979842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1c9ebdf-6c5c-46f9-a8b0-d40daf0a8dc5 · outbound

This paper cites The cifar-10 dataset,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers The cifar-10 dataset,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:21.965393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c91688db-3921-4659-9f99-8ec3f1d6f87c · outbound

This paper cites Pin-3d: a physical synthesis and post-layout optimization flow for heterogeneous monolithic 3d ics,.

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers Pin-3d: a physical synthesis and post-layout optimization flow for heterogeneous monolithic 3d ics,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:21.642571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 8aa85490-e6c9-4ede-adb9-6bb8a5c07066 · inbound

SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks cites this paper.

SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:43:25.901211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a9c8c84-c348-4d5d-8571-33d990d5e2c5 · inbound

Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts cites this paper.

Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:51:39.113913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.