Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2111.10770.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T17:50:17.692884Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T00:39:16.827760Z
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 71c18002-10b7-4480-ae80-e78d946d50a3 · inbound
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT cites this paper.
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T22:55:51.706532Z
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 96af4684-8215-4e9a-8852-6eef60da8d0a · inbound
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs cites this paper.
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T17:50:17.692884Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34e0a73a-48de-4567-9fb0-9e71385e0aa8 · inbound
CIMple: Standard-cell SRAM-based CIM with LUT-based split softmax for attention acceleration cites this paper.
CIMple: Standard-cell SRAM-based CIM with LUT-based split softmax for attention acceleration Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T07:57:15.109584Z
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 82e9fb37-6a34-48bb-9705-0eb001a383a9 · inbound
Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices cites this paper.
Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T00:39:16.830406Z
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 20ccdf76-502c-4a91-9a38-365f46ff28b5 · inbound
A Systolic Array Architecture for Nonlinear Activation Functions and Softmax Computation using Chebyshev Polynomials cites this paper.
A Systolic Array Architecture for Nonlinear Activation Functions and Softmax Computation using Chebyshev Polynomials Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:54:17.743284Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.