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Paper Citation Record · LEDGER

Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2406.08413.

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

pith.paper-citation-record.v1
2406.08413 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:52.561193Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

9
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d719e35a-261d-4984-b3e4-1074b5865fb9 · inbound

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO cites this paper.

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 127

Resolution
unresolved
no resolver link, observed 2026-08-07T04:57:52.561193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:57:52.561193Z digest=sha256:19e4d3b3e0ac147cce9f765c4fc8f21e41ac253237cb182f18466ab22e93615e

Observation 9064938b-af1a-41b0-9766-ae704dcf6a9f · inbound

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling cites this paper.

AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.489848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:24:53.489848Z digest=sha256:ebe2b6fe85c67f8b44b8e1e204e2105a0a74b635d4190a4bac49655342284e88

Observation 313ba47d-2924-4ce0-8580-18c49df2de96 · inbound

DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs cites this paper.

DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T14:59:21.220570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:59:21.220570Z digest=sha256:042af4dd89f6e620504d2bfbd6d210492aef2345e6e74894787b53fd43b4918e

Observation fdf80982-8ef3-437a-85b8-8eca0a9fb394 · inbound

From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill cites this paper.

From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:51:08.930601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T08:47:29.759674Z digest=sha256:5b4dc599ff42bceef3cb70cf84d99b4e6464cdcb9207b062d9f9299eafd72157

Observation 004f0709-0cbf-42c5-b11c-1968459a6306 · inbound

Increased endurance of nonvolatile photonics enabled by nanostructured phase-change materials cites this paper.

Increased endurance of nonvolatile photonics enabled by nanostructured phase-change materials Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:51:52.592824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T17:24:15.164171Z digest=sha256:461c8054ca5bc5237bf7d4a5988ec968e6b6ae72bcb1252d65354e7a4c95152f

Observation 2330c259-9e30-400e-a8b6-b545bde908bf · inbound

DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review cites this paper.

DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-15T17:20:10.420212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T17:19:38.627340Z digest=sha256:4cd2a1690c360a93586ddaaaeb6ce973f3ee219498dae870ef0bd7e4bab40c9f

Observation 64964b89-24e6-4add-85cb-beaf12876fe7 · inbound

DAK: Direct-Access-Enabled GPU Memory Offloading with Optimal Efficiency for LLM Inference cites this paper.

DAK: Direct-Access-Enabled GPU Memory Offloading with Optimal Efficiency for LLM Inference Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-12T00:36:13.817928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-07T15:00:23.910898Z digest=sha256:cfdea760ed4bc2e523a29c20a5cf8121c3d29074ff94a3fc6a2656505af09139

Observation 1267c1f8-d439-4c7e-8c2c-06081dfb7530 · inbound

An Unsupervised Machine Learning-based Framework for Wafer Scale Variability Analysis and Performance Prediction of Ferroelectric Hf0.5Zr0.5O2 Thin Film Capacitors cites this paper.

An Unsupervised Machine Learning-based Framework for Wafer Scale Variability Analysis and Performance Prediction of Ferroelectric Hf0.5Zr0.5O2 Thin Film Capacitors Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-09T19:05:10.899003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T18:45:07.928250Z digest=sha256:ebf16bbee3d4850875c27d153aa466f7c73b36b9760ac4e5b0ed6349f276632f

Observation 12f72179-af77-4f6b-9d36-e39beb185883 · inbound

Chips in the Flatland : 2D Semiconductors for Future Computing Electronic cites this paper.

Chips in the Flatland : 2D Semiconductors for Future Computing Electronic Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 156

Resolution
verified exact
arxiv_id, observed 2026-06-29T15:03:31.523454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T15:00:44.107706Z digest=sha256:09a0c293824d2ab37c09064b2a8ba2fb42e552f5bc098189843f9058dff10103

Observation 9a9b0409-9c6a-4964-a33a-41a325ab2c7f · inbound

Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture cites this paper.

Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 158

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:36:09.333689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-28T22:12:13.114405Z digest=sha256:16b7ae5486c23b8ad2f4187bae9cbc79940ea0f166a2acf92e27606926a89cc9

Observation f5f0d840-1189-45e3-9e9f-c8d0483b8b74 · inbound

A First-Principles Theory of Slow Thinking and Active Perception cites this paper.

A First-Principles Theory of Slow Thinking and Active Perception Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 172

Resolution
verified exact
local_arxiv, observed 2026-07-10T11:37:03.314288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-10T11:32:24.374377Z digest=sha256:6d874ff550ede580bdfd72c5733a62af5bbb3e5832d30bf46aa0c37079baa5fa

Observation 930db989-f3df-49a3-b003-3ef3e440f69a · inbound

FastTPS: An Optimized Method for LLM Token Phase for AI accelerators cites this paper.

FastTPS: An Optimized Method for LLM Token Phase for AI accelerators Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 44

Resolution
unresolved
no resolver link, observed 2026-07-14T06:16:09.070414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T06:16:09.070414Z digest=sha256:599b584985b6207a7561a12ac194cbe8c9cfa31aa7e13b95092a81fa84ebcf9a

Observation f6f83c22-51b8-4517-8890-71eb6f4cf4a1 · inbound

ComFuse: Fusing Complex Memory-Intensive Subgraphs with Compute-Intensive Kernels For Modern GPU Architectures cites this paper.

ComFuse: Fusing Complex Memory-Intensive Subgraphs with Compute-Intensive Kernels For Modern GPU Architectures Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-05T17:14:17.119250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:14:17.119250Z digest=sha256:77dcf6a53f1992cf456b8984c86d695f8374d1e377a6fee3f834e388ece646aa