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Paper Citation Record · LEDGER

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration

As of 7 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 0 inbound Pith citation observations for arXiv:2607.08993.

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

pith.paper-citation-record.v1
2607.08993 v1

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T01:10:03.032181Z

measured 76 of 76 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

76 of 76 outbound references displayed

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  • verified fuzzy0
  • unresolved76
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d0b5da88-3461-4e1b-ba7c-1c43d0b44aab · outbound

This paper cites AWQ GitHub repository.https://github.com/mit-han-lab/llm- awq/.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration AWQ GitHub repository.https://github.com/mit-han-lab/llm- awq/

Reference 1

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:43e9b7c956d199618c78139f6934417469fff28bd3f89d20bbec97e6295c8e54

Observation 75cb4bde-1478-4aa0-9a54-6541ff851bb5 · outbound

This paper cites NVIDIA H100 NVL GPU.https://www.nvidia.com/content/dam/ en-zz/Solutions/Data-Center/h100/PB-11773-001_v01.pdf.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration NVIDIA H100 NVL GPU.https://www.nvidia.com/content/dam/ en-zz/Solutions/Data-Center/h100/PB-11773-001_v01.pdf

Reference 2

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:5f734390682f87125cfe5d1151ebf10510492afc3057aa4a82b1cf930abac678

Observation e32f8ed7-e5e3-4ce6-a0c8-eb02cfa8b192 · outbound

This paper cites vLLM GitHub repository.https://github.com/vllm-project/vllm/.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration vLLM GitHub repository.https://github.com/vllm-project/vllm/

Reference 3

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:92669f5339f806c8ae18cee7bce686355679b4692084bbdc1fdc81520c8218b4

Observation 9fe71c5b-fa21-4327-adf6-2c68779d7cc7 · outbound

This paper cites FloTHERM.https://plm.sw.siemens.com/.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration FloTHERM.https://plm.sw.siemens.com/

Reference 4

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:b1884ab2b539abbac4dab17a3abf05b6b7544113e22ba61ec5c6d81c3ca981a3

Observation 0834a8ef-37d7-4dc4-8520-bba7e97f0cf6 · outbound

This paper cites NVIDIA Management Library (NVML).https://developer.nvidia.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration NVIDIA Management Library (NVML).https://developer.nvidia

Reference 5

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:b1cb9dd4a98ba0bd3f4a7ae264adeea547a2356e9049808b0862bf4f477db046

Observation 8a691a48-1048-4c3a-a072-e04c04d4baca · outbound

This paper cites NVIDIA NSight Compute.https://developer.nvidia.com/nsight- compute/.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration NVIDIA NSight Compute.https://developer.nvidia.com/nsight- compute/

Reference 6

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:70fd044f7c141d4d4aebf1f998bc66496454347d79835c4fe518a96b431ebc47

Observation 5732377d-1e4b-4b31-b50b-cfffbde9943c · outbound

This paper cites NVIDIA NSight Systems.https://developer.nvidia.com/nsight- systems/.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration NVIDIA NSight Systems.https://developer.nvidia.com/nsight- systems/

Reference 7

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:89335fe2e76bb655c70b5c46b02464e6347842d7cede9894727d5d533b61b280

Observation ad02d7b2-92de-47cd-953b-c9fb3f20ee56 · outbound

This paper cites Synopsys Design Compiler.https://www.synopsys.com/.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Synopsys Design Compiler.https://www.synopsys.com/

Reference 8

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:08d34fbe1705b820db6bc6f61f0dfd9caa2d08390c14e07518d607d6631f4e1a

Observation 364ad72c-885f-4461-a02e-de699e9cd544 · outbound

This paper cites TensorRT-Weight-only-quantization.https://developer.nvidia.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration TensorRT-Weight-only-quantization.https://developer.nvidia

Reference 9

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:a023294685a82497e497f32db37f38b417a8dda390d053ca7b1dea6d2ef8a94d

Observation 8eaf4db0-5bc8-4285-b113-eeaed3c7ea1a · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 10

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:4bc7ef5b2ec55f1395c7b76c5b35a64590c5f589fea39baada573a6d8da30e3b

Observation 9e0fb216-1738-4ce7-ab87-26bbea732c5d · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 11

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:23c5e60b073b156c1b7edfe9e5f47924247400aebb3a8072e65166a4a848d919

Observation f4c52379-ea00-4419-92e4-8a20241bd9e4 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 12

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:12cf67983a41f51b21b53467866ea5eb03dbbc342a46baa5b6a9ee2f3370974a

Observation b20419c8-df38-4b1f-96c6-757744719bf3 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 13

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:f268e5e191b34367edffe55adf23f8c8943a4aa6ed846895443f27af8eaf63d0

Observation d73e7b3d-48ab-4e0b-8ac7-9010744c85f8 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 14

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:2026ddb003b0463995c1402113c8f491bb6b5c78c0c0b8a20562e8181022d7d8

Observation b4700904-5106-4134-923c-ade1f166edaf · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 15

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:75e432bfc3dd1b667bf30d2dc3f9006350f93b17a02f9e6bacd716e8d45c858b

Observation a615ed01-ab18-4a72-9243-85f86be8cb49 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 16

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:ef68e9b1b5d8591ea92d797459f61a32d75f34a70b756350b23dd28779b2159b

Observation 06d0d6f4-93c2-4097-bc99-994db77fcc43 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 17

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:17aeb4b448491fcb54530996656042ae7eb298d2add6ac4b3c38498456cb0af7

Observation aa7457c3-a334-4755-bcba-67cf9bc0e0bc · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 18

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:d19f77fdf31165367f30407b327ffcca90b031addf78292612f0ecfa5f54c336

Observation 62199408-a4ae-4a31-bc47-668e1b04ea85 · outbound

This paper cites int8 (): 8-bit matrix multiplication for transformers at scale.Advances in neural information processing systems35 (2022), 30318–30332.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration int8 (): 8-bit matrix multiplication for transformers at scale.Advances in neural information processing systems35 (2022), 30318–30332

Reference 19

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:05e38f099533db8073439c8cf5189a07041342065bca22b86b654ec196d46da5

Observation eae854f0-15a1-4525-89a3-866236a892c1 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 20

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:49ff79af1a8effb76500538bcd5178148ee770e13375ec15de18457bf99e86ac

Observation e3f26909-a5f9-4f0f-b0b8-05684a9315eb · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 21

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:3dc5482cad94867437003b617fdf11f5d227d037a372e9a7471956cde8793a9a

Observation b59f9414-ac4d-458b-a75a-2a4c0cd259ea · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 22

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:824ffbd627d96bfa88261e41caad101af5345d6fb88a0c32a7248a31bc16a8c7

Observation c5470fe1-aa42-468c-9014-771ae014542c · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 23

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:120633b10841118309d4ee61a16ce3bf0b7ca4fb3894881f433d0c5784c7d4df

Observation e8cad5e9-a754-4629-a443-ddae322c182e · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 24

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:000f4a42eb2115754167f5424d6e1be0c55a1f9196ea32ec026c261fcc9bee8a

Observation 39c9cb58-7ea9-4cb6-a102-43e231dbd6b2 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 25

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:d9bfde0ae3074743b4127b089cff97cd8f0aded5329419ec1bf6401773058593

Observation 2d05d036-7c23-4829-8711-39ea3c512b63 · outbound

This paper cites InProceedings of the IEEE conference on computer vision and pattern recognition.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration InProceedings of the IEEE conference on computer vision and pattern recognition

Reference 26

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:1dd3fe4b84ca762ab9558c4f6fad09a56dd4f7a592f7caa4b652ee3f296b81fa

Observation 4d7daa7a-264a-4b8e-8ed7-4f36d87f3f69 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 27

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:13ca011d905d71950d2b7c4975ff5be09d036f21d223425d090f6c48a556b447

Observation 54c32933-5f26-4daf-add0-51d2c0ed6151 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 28

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:051270761538ce6eaec177237ac226bd8ee280e84dba99bb4b738fc8329f6ddc

Observation 39bb0f80-d69c-4e7a-99c3-571c7bd01733 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 29

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:3281dc6234b264c2fa7035102d31bcb575cf47f57e8b66a8e8f572eb6878e26f

Observation ad10a104-3c2b-42cf-b3ae-8e9a5289b9b0 · outbound

This paper cites Mistral 7B.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Mistral 7B

Reference 30

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Observation ab16eb30-2ff8-40ca-a949-64208d19e93a · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 31

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:5c4c6338cd9c1da355c8668375f73c91e889648b92ed1794b120b9e38c4e8d6d

Observation b60fbefa-2b07-40bd-94a2-baa13222deaa · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 32

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Observation 30e63d77-7751-4ebf-8c4f-0973aa7fe04f · outbound

This paper cites Scaling Laws for Neural Language Models.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Scaling Laws for Neural Language Models

Reference 33

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:4348bc47bc68e868ed9af2f92b0d9da7e569d4a201b3ef36206f0db5c6857301

Observation da63b521-8e3f-4dfa-ac1e-9c2115f95ec7 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 34

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:85b9adb9b0293e8d4121fec90e384174abc300d09ee34b4478c2ed3a0ee6d049

Observation 99b8a849-93a2-4da8-8cea-84f48c3cd097 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 35

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:b1c6ebf6a75244b6e3cf49e4595092939418d5dfb76f3a0b3263bcf3e479bbe8

Observation cac02070-cbb8-444d-a001-f36861d197c5 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 36

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:6c94aee61a1dad05b23df827c259e98734b1d31c77ec067ec96f24b2deda5cf4

Observation 8f8ecf25-754e-4290-a1a0-249cdffe1e21 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 37

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:8e7e19f4f122c5ef20112e63984699dc67c8bc9a6b6134be3fbdc80173d3ccfe

Observation eda5d5ed-c6f7-473f-83d0-bae92b3b2c37 · outbound

This paper cites InProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration InProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Reference 38

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:d6c96e34af9eb3bed495465aa15c55a244bb331cc99bbeb930d19ffdce295f02

Observation d1a0d435-c214-4a66-9637-94f85c195e8f · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 39

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:69cd7ba4a1aa8020fb305d15a03bfe4d0f2d946a0785a09f99fb4285508a8b4d

Observation 91ab3514-5ab9-417b-bd6b-a58cc4aea220 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration SqueezeLLM: Dense-and-Sparse Quantization

Reference 40

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:b1808e1e35b2fe26e21f7eec65d0cd8a7fb0830fc81aff8f9a88e9d19e94c7f4

Observation f490cb86-8e3d-4172-bceb-09c3baccc34d · outbound

This paper cites QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference

Reference 41

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:bf2aab4977dd7aa594f4ccd49cdea65209be6e28ba84ae54664ca7748c6a3040

Observation f7dec1dd-1d58-4a1b-9ef3-6ad2c3e1e001 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 42

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:4dac974c3197d2a73dbd95e0ff8182a1b13fda2711a206718b20c2b170cced6f

Observation e816ef1b-7fdb-4471-861d-9691d04c1a61 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 43

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:32e3df80c1be48e24f8ccd8b245b57fe4585de69faef2d0e85f801565646c58a

Observation 8b64c419-c8a0-42e5-8637-620c9d6d7203 · outbound

This paper cites InProceedings of the 29th symposium on operating systems principles.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration InProceedings of the 29th symposium on operating systems principles

Reference 44

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Observation 41acf1b4-ec0f-42d2-b850-c3d67c808778 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 45

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:ae309ca7e4d237c19654b9a0f1b7d00b702172b5edba9a042f3c34a5c8634472

Observation 3ae34834-5a8e-4042-aa34-1384379d097a · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 46

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:aa98e6e986b6ef90e9e7d2d21502ff6b868265bb988913abab64cdb75913d2ac

Observation 7541a11a-411f-469e-9d96-8e7e278cc77f · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 47

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:470af24e1a2af915068a7c334d4fec74abace5d58e76aeae4d2675aae1afb295

Observation b1cd48d4-d862-4eae-9072-1bc8fd4d00ee · outbound

This paper cites QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 48

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:8d3321ddc2f01ff120f468f1249f8ead987b987622f6daab6709eeb7b35f5763

Observation 669e74c3-96c1-4e65-a054-3d87dc1fc759 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 49

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:cb4e1ea8a765574c2e877dcc090a31f3cc78101c7988312a5366f1d453bc70e7

Observation 1754fb07-c4dc-4794-a7a4-574e7ca1d8c2 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 50

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:102c0f2365dbcb744a614d44bce581ec55f4a6a56e1559f6cf6181b1afb56234

Observation 109cca2d-fb6e-4581-b0ea-04d9c188e1fe · outbound

This paper cites Custom 8-bit floating point value format for reducing shared memory bank conflict in approximate nearest neighbor search.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Custom 8-bit floating point value format for reducing shared memory bank conflict in approximate nearest neighbor search

Reference 51

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:d4c809b58a60fccb046b64cb9951287c2fd0f98483c629db3f3cf83a5644338c

Observation 136220bb-1fef-44b7-9aba-4e218fb10ad2 · outbound

This paper cites TorchAO: PyTorch-Native Training-to-Serving Model Optimization.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration TorchAO: PyTorch-Native Training-to-Serving Model Optimization

Reference 52

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:63d5986920290da993137c4afd0d31d62d2587351a13c9328322f0da4c980b21

Observation 20ce84db-bde6-4c76-8ab3-534611cca6c7 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 53

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:3c0d28ed9a937af3fa1cfc100b2a0b1e0ee5f3bb06339211c3373ed9e303f80d

Observation 08d576a2-5722-4cce-9b40-07d1df0ff7e7 · outbound

This paper cites LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models

Reference 54

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:45b2244acd559756cb2d14e54af48fa4b5f9f0a9535dce34830ea7ce29bcd024

Observation 7c84cb65-d0f1-4084-9d7f-a044577e5025 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 55

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:01664e9f0c7e19d9db2bca63c9c8fd73a9537ad9c859b7ea0f323641f8ead970

Observation d31f3997-2717-4fba-9eb4-d29a815ff4bb · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 56

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:fcfa113967ee556b94832bd99aba46d44b7a4de968cc903bd968a2b5d830a1c0

Observation be9634d8-71ee-496e-95e2-4bc3d0cc10b6 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 57

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:a117a78a9faef2571ad7e2c3e27a7087125661be828490c066b332d7dc51a145

Observation ecb37a61-e90a-48eb-828e-db65c7bc8149 · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 58

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:400d95edb75e60cd2824d563d7444c3acc4b414b6766320f348b49ce68010583

Observation a3852807-a9ec-4c5a-b593-97bf3d9065fc · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 59

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:63ae26d2abe94ff230e13dfaf4740ccd7ca31fea6f8d6cf798568eb664c8acdc

Observation d018821f-a67e-46a5-83fc-3b18162893b6 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 60

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:4edfea8c48e5f1685432377fa3d12bf10908958d53c6f5624b26ad08bbca1c2a

Observation b7c99f50-5133-4152-96cb-0d1c4fb7d7a2 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration LLaMA: Open and Efficient Foundation Language Models

Reference 61

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:5d67b245bb2476951d55d2f5c28bff7a18bc8b3a8c98030ca401a0a72152a238

Observation 9a4b6702-558f-42fe-bc96-ad54b479efb5 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 62

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:6f12f3d4801fa74251552c521df4114bb3c9815a367eebb15ca44dddc40c9ce4

Observation be6d6557-35cd-46b2-9265-59c83c760d20 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 63

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:b7f7a295c9d33ba34d5dd1031e4738cf92dfc9ca6546e822bd4a6460f0bd54b7

Observation 9de50d70-9815-49b2-b999-dacac0297846 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 64

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:accbc68a24948ce869a74efec369b0c7d8b6744b9b75fe2bc996807d505f7c5f

Observation 3c86b8dc-6499-472a-9169-a1acafae269b · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 65

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:96b77dfe4912d35246923e7c37cfe9c09a48aa2b3ac5063bb4590181441324d9

Observation bd387cc3-d292-46f2-99a4-6da9b54f602f · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 66

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:47b786b8867a37b83bd135b7c7c55dd2905b5688055673d691f322c110f3246d

Observation 3cbed698-3d63-4a38-9eee-f8d4d27f6faa · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 67

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:3e6b597e8b149920f8a971b87796a0a24226fe0e2e0718cc3b7a3361acdce5a3

Observation 427d0969-7cae-4a4c-823b-c7d78a072d25 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 68

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:135e2d9c80d85a2d830ad4132d2e1872095caa236c1a7269b26643a0707a1a1a

Observation 92a29df8-67f2-4c9d-8ecf-74a21687d03e · outbound

This paper cites Qwen3 Technical Report.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Qwen3 Technical Report

Reference 69

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:be65bb64583c7c0b5026be51eca3f6b9bff25c54425da20f9fc62aedd19fe331

Observation e651f12e-b2dd-48c8-b6ce-42bf956a1d32 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 70

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:22ba5e481b5ed9f18f19333d3afe9e1395d01e6cae463c2bcd4af9a6a9c53426

Observation b486abc4-7a29-4502-8f1f-6183d49cd646 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration OPT: Open Pre-trained Transformer Language Models

Reference 71

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:069a6c1598bb2b96872b9fe234d6cacad903f88da4d4404d1c9e2e31d7ea39ea

Observation 176971e1-129e-44fe-9653-e42be1804a59 · outbound

This paper cites MixPE: Quantization and Hardware Co-design for Efficient LLM Inference.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration MixPE: Quantization and Hardware Co-design for Efficient LLM Inference

Reference 72

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:580ce91e4e658cc16adeda5dea29189782b0feb73b0a9d256c51ac36317edbc8

Observation 36fb5bac-05d9-43d0-a6a8-cc62a15caf02 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 73

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:bacead99557a7b9d4ace651f13001733b3ba530f0cb11f7e3cd1f43f5460a2e6

Observation a3946790-ddaa-4c53-8f9c-2b36cf80babe · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 74

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:98009255fbe76972aab270db147924faef2be4ffe8e11af51b0034d4cc772ec6

Observation 737934a7-5e78-4e67-84bd-4936196c692b · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 75

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:9153582e80ef0106f05794487d4f385f9c0dd078e8eb0c251f849baf4df8e213

Observation 765e83b0-8928-4bf3-a551-1317f99b9402 · outbound

This paper cites an unresolved cited work.

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration Unresolved cited work

Reference 76

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source=pdf_text observed=2026-07-13T01:10:03.032181Z digest=sha256:9d2077fbfa7b475f6240d4abb69079f31e87b7a56e78c839e5c5c9980d30a9ac

Pith citing papers

No inbound Pith citation observations are available.