Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2110.05929.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:07:11.461775Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T00:55:12.061217Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 3544fbbc-aced-43a0-88aa-292bef54c0b1 · inbound
STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design cites this paper.
STI-SNN: A 0.14 GOPS/W/PE Single-Timestep Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:07:11.461775Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e6c50cf-cd5d-47a7-aebe-0021dd4f8e5f · inbound
SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization cites this paper.
SDSNN: A Single-Timestep Spiking Neural Network with Self-Dropping Neuron and Bayesian Optimization One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:19:19.777446Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77741013-b8ef-471b-aad6-080e827d33e1 · inbound
A PVT-Resilient Subthreshold SRAM-Based In-Memory Computing Accelerator with In-Situ Regulation for Energy-Efficient Spiking Neural Networks cites this paper.
A PVT-Resilient Subthreshold SRAM-Based In-Memory Computing Accelerator with In-Situ Regulation for Energy-Efficient Spiking Neural Networks One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:47:17.435284Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b03b5901-6b1b-48ca-9163-db93652b95eb · inbound
SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning cites this paper.
SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T00:55:12.062972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.