Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:13:45.206239Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2507.11506.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:13:45.206239Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
78 of 78 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Open Neural Network Exchange format
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques NVIDIA Hopper Architecture In-Depth
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Abdelhafez, Christopher Zimmer, Sudharshan S
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques WaferLLM: Large Language Model Inference at Wafer Scale
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Dissecting the Graphcore IPU Architecture via Microbenchmarking
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques ClusterFusion: Expanding Operator Fusion Scope for LLM Inference via Cluster-Level Collective Primitive
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ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Unresolved cited work
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Observation bee40705-ecea-4b9b-a482-ad335726b609 · outbound
ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques benchmark_scripts/generate_data_from_sim.py
Reference 74
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3fc12d40-93a1-4363-8bc4-f815b48e9c31 · outbound
ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques InProceedings of the Seventh Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA’95)
Reference 1995
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1cc94341-e15f-4d67-b372-90639b9dfe10 · outbound
ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques InProceedings of the 23rd IEEE Symposium on High Performance Computer Architecture (HPCA’17)
Reference 2017
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a029c739-faeb-4362-89c3-6b9bb1841062 · outbound
ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques InProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS’22)
Reference 2022
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c829d11f-5f15-498e-ae38-e783dbb1d33e · outbound
ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques Gemma: Open Models Based on Gemini Research and Technology
Reference 2024
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.