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Paper Citation Record · LEDGER

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

As of 16 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 1 inbound Pith citation observation for arXiv:2411.11745.

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

pith.paper-citation-record.v1
2411.11745 v2

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:18:05.706979Z

measured 79 of 79 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:46:23.173339Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T21:46:23.280942Z

Reference resolution

78 of 78 outbound references displayed

  • verified exact2
  • verified fuzzy46
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5980f8c2-9ac9-4b74-8e65-5e0c6bca93cd · outbound

This paper cites 01-ai/yi-6b.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 01-ai/yi-6b

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation f3db5b81-78fd-46ec-817c-fb981a9bd287 · outbound

This paper cites BitMoD Artifacts,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration BitMoD Artifacts,

Reference 2

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doi, observed 2026-08-12T18:18:05.748429Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 85c2b403-7f55-4119-9554-9a3dc3c2039f · outbound

This paper cites Bit-pragmatic deep neural network computing,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Bit-pragmatic deep neural network computing,

Reference 3

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b1d1d050-6357-4e44-a76b-6576bf2429e7 · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.277708Z digest=sha256:29ce8271285f30dd61905062dce2ddba0d21a7de4ea9b16236f06d0a38ba5740

Observation 283835d1-d567-4638-a37d-c325ce5fac9f · outbound

This paper cites FPRaker: A process- ing element for accelerating neural network training,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration FPRaker: A process- ing element for accelerating neural network training,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.284471Z digest=sha256:51548144ffbf582cd73ae3669e45f1e202a9361ca30c043cf670a756179b9467

Observation 1ab82118-8b93-4fd4-b0b9-4639df009127 · outbound

This paper cites CACTI 7: New tools for interconnect exploration in innovative off-chip memories,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration CACTI 7: New tools for interconnect exploration in innovative off-chip memories,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8247cbb4-f2c7-479e-af6c-7e86b6ba2857 · outbound

This paper cites PIQA: Reasoning about Physical Commonsense in Natural Language.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration PIQA: Reasoning about Physical Commonsense in Natural Language

Reference 7

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Observation 1bb55cd6-20b9-410b-85a1-fc2509bf2904 · outbound

This paper cites A signed binary multiplication technique,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration A signed binary multiplication technique,

Reference 8

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation e693a695-8dfa-455c-96f3-26882d34f732 · outbound

This paper cites Language Models are Few-Shot Learners,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Language Models are Few-Shot Learners,

Reference 9

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c8357a80-ce17-4376-8e94-8a10bda052d8 · outbound

This paper cites QuIP: 2-Bit Quantization of Large Language Models With Guarantees.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration QuIP: 2-Bit Quantization of Large Language Models With Guarantees

Reference 10

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source=pdf_text observed=2026-08-12T18:18:05.311475Z digest=sha256:9fcd95796925d9d4561288272f9fafa9ceeef9c238ea0658ed8f40a1235fbcf0

Observation 479af3e7-051c-426f-9b63-c405f4b01b9a · outbound

This paper cites EfficientQAT: Efficient Quantization-Aware Training for Large Language Models.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 11

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source=pdf_text observed=2026-08-12T18:18:05.317424Z digest=sha256:c7629123838ac5bf08024f167444aedd1d1131a8e5cb519fe01af3a613262ffb

Observation 2dccfd08-f436-4a41-9d2f-7da58dade405 · outbound

This paper cites BBS: Bi-directional bit-level sparsity for deep learning acceleration,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration BBS: Bi-directional bit-level sparsity for deep learning acceleration,

Reference 12

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raw_fallback, observed 2026-08-12T18:18:07.001260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation cee050f3-4088-47be-b257-5ebbad780cd2 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 13

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Observation 8dd416d3-fa3a-4749-bfbb-8be9bd237287 · outbound

This paper cites VS-Quant: Per-vector scaled quantization for accurate low-precision neural network inference,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration VS-Quant: Per-vector scaled quantization for accurate low-precision neural network inference,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 3ee0bdbf-5c80-4a4f-a57f-37e70919a3b5 · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 15

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source=pdf_text observed=2026-08-12T18:18:05.345398Z digest=sha256:26eefba84ccc6689f0da7c59b0dd4a4bf418baf5d12da36b27f9419d440a2d58

Observation 5d175240-32ff-4a1c-b08a-03049c74372f · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 8-bit Optimizers via Block-wise Quantization

Reference 16

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source=pdf_text observed=2026-08-12T18:18:05.352436Z digest=sha256:2f47bdb586d789b6c34dbbd2bb19a9450f1ffde16d595dfb02873929ebb03cf4

Observation d5de9375-3bab-4329-9031-273fd6e52c1e · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration QLoRA: Efficient Finetuning of Quantized LLMs

Reference 17

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source=pdf_text observed=2026-08-12T18:18:05.360468Z digest=sha256:8e6d368377809170640787f877322db951318a64bd9643224ff2ff429ba9653d

Observation 8db960d5-1417-4ef1-adb4-636f12b28563 · outbound

This paper cites Documenting large webtext corpora: A case study on the colossal clean crawled corpus,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Documenting large webtext corpora: A case study on the colossal clean crawled corpus,

Reference 18

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b6cda7ca-1185-4e43-876d-878d71f5eb86 · outbound

This paper cites Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs

Reference 19

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source=pdf_text observed=2026-08-12T18:18:05.378635Z digest=sha256:fd078043bcae73402a4fcb00007379a4ef0110058aca89fdb731be0e2e59d3b6

Observation 3545e30f-95d4-4a26-bbc4-f21d6b72dc04 · outbound

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

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 20

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Observation ddfe7364-2d0d-436f-ab60-51c3a907f32d · outbound

This paper cites A framework for few-shot language model evaluation,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration A framework for few-shot language model evaluation,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.390678Z digest=sha256:051ee2a8229c705252ff7f82eb7e5db07d7ecc3f3eb7046117a05c9ee7c162c5

Observation be480d8e-aee2-44cd-b3a2-f461bf46836d · outbound

This paper cites Ai and memory wall,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Ai and memory wall,

Reference 22

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Observation f0a69e1c-d141-402f-b7f2-99c277903d3e · outbound

This paper cites SparTen: A sparse tensor accelerator for convolutional neural net- works,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration SparTen: A sparse tensor accelerator for convolutional neural net- works,

Reference 23

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.409046Z digest=sha256:0273e4b34c57b22cd1267fcc81cbfcd219cbb6662600e93b09006b0b2aaf93e3

Observation 0d38f1b3-3691-420e-aee0-19b0f326cda5 · outbound

This paper cites Eureka: Efficient tensor cores for one-sided unstructured sparsity in dnn inference,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Eureka: Efficient tensor cores for one-sided unstructured sparsity in dnn inference,

Reference 24

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.413936Z digest=sha256:f2fdc0e08f257ad34f3cb8bffae0a6c86ebd15a444ec1ca3e1507ad6a8c5fe39

Observation e73d0ec0-f500-4865-9674-ce9d76d013be · outbound

This paper cites OliVe: Accelerating large language models via hardware-friendly outlier-victim pair quantization,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration OliVe: Accelerating large language models via hardware-friendly outlier-victim pair quantization,

Reference 25

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.420014Z digest=sha256:2318d2ea577191a687747c49224c2422c8bb5b129ff1ef931f77dfff7f3e2320

Observation f5e1e0db-32d5-40fd-b2f0-64bbb5dd0d49 · outbound

This paper cites ANT: Exploiting adaptive numerical data type for low-bit deep neural network quantization,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration ANT: Exploiting adaptive numerical data type for low-bit deep neural network quantization,

Reference 26

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.426405Z digest=sha256:0121ae46e49ff3afe88e5c3e489af38be9070415c5e6ef195c2cd3c76e3c0172

Observation ee59c750-11f7-459f-9815-ebfb865b8435 · outbound

This paper cites FIGNA: Integer unit-based accelerator design for fp-int gemm preserving numerical accuracy,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration FIGNA: Integer unit-based accelerator design for fp-int gemm preserving numerical accuracy,

Reference 27

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source=pdf_text observed=2026-08-12T18:18:05.433723Z digest=sha256:cc9944439a447a93f88376fa066cc5d26834a43b713d5bcfe5293ef3262dbb38

Observation 2a54e312-1d68-430f-975c-db83cf9d2b67 · outbound

This paper cites Stripes: Bit-serial deep neural network computing,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Stripes: Bit-serial deep neural network computing,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.440602Z digest=sha256:37cdb474115384288d9d5f8032bf14a7a7cd601dbe74adf9255fc1b8014a11b2

Observation c003ceb1-e834-4fa9-a93f-2963c7bb798f · outbound

This paper cites DRAMsim3: A cycle-accurate, thermal-capable dram simulator,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration DRAMsim3: A cycle-accurate, thermal-capable dram simulator,

Reference 29

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.447222Z digest=sha256:f563023dd6106db36447faee0bef7ff0e5465dc0a2e03ea723c75eff8537e319

Observation c7bfffa2-5e67-44db-af87-c87d603e6992 · outbound

This paper cites AWQ: Activation-aware weight quanti- zation for llm compression and acceleration,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration AWQ: Activation-aware weight quanti- zation for llm compression and acceleration,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.452627Z digest=sha256:28b3ad4752a7e5bb3d443857c4ee2713319366b4d1de0f24569367dfb38661ff

Observation d62349f3-65fb-469f-a062-cce71e198d54 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 31

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source=pdf_text observed=2026-08-12T18:18:05.459059Z digest=sha256:a8387970c8f0a1018a9d5d7c40266cc9206f16208ebee05367915c5df091b2fe

Observation f5a1814d-ea1c-46dd-a8c9-d88538f1ba26 · outbound

This paper cites Torch2Chip: An end-to-end customizable deep neural network compression and deployment toolkit for prototype hardware accelerator design,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Torch2Chip: An end-to-end customizable deep neural network compression and deployment toolkit for prototype hardware accelerator design,

Reference 32

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raw_fallback, observed 2026-08-12T18:18:06.812130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.464500Z digest=sha256:971b781bc30148903362dd5e93ef31c48e61c5ecbfd7e53b9b23f37f5add4706

Observation 5b3eedc9-d35b-4eb3-b600-5fc5a3d69f79 · outbound

This paper cites Pointer Sentinel Mixture Models.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Pointer Sentinel Mixture Models

Reference 33

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source=pdf_text observed=2026-08-12T18:18:05.469720Z digest=sha256:ebadca68d5bf9c61224ee3ab4c51338d9ce1e3fa3c3082b81e1b262035a29ec0

Observation 81d9296d-5f45-4c8d-863f-3f33405fbfd7 · outbound

This paper cites Meta llama.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Meta llama

Reference 34

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.475392Z digest=sha256:4dbf74aa67a36082773793ddb35496b9a4d936da5627602fd35f501e720e8c16

Observation 16847e1a-113b-4511-9d86-e791d99069ae · outbound

This paper cites Meta llama 3.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Meta llama 3

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.782651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.480490Z digest=sha256:e26fc75514304660952c816f502ac82263104969822c4478cddb46735dfa83bf

Observation 403f10ea-6298-4d20-b183-be7cc6748a9a · outbound

This paper cites microsoft/phi-2.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration microsoft/phi-2

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.767329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.486820Z digest=sha256:5828ad87fa225042380d2026c2ff20369a1dc04385ab6bb6b45e091a8d944cc2

Observation b725fec7-f52c-423a-8b77-a3f1e5b557ea · outbound

This paper cites Jetson TX2 Module.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Jetson TX2 Module

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.752027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.492944Z digest=sha256:ba9aa8f4ebcae79127166631c6ccc515249936d40e7cb5b3e7ea86306b388ea8

Observation c80b6c48-46be-4ec0-a7e5-2730e6c31b47 · outbound

This paper cites OCP Microscaling Formats (MX) Specification.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration OCP Microscaling Formats (MX) Specification

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.736192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.498944Z digest=sha256:78960b0b55fcbab304576bf3c8105c0afe256ee87ebde3aba05601d16b567f6d

Observation c30a3d7d-d8fc-43e4-9b0b-ef928c26f6e8 · outbound

This paper cites Energy-efficient neural network accel- erator based on outlier-aware low-precision computation,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Energy-efficient neural network accel- erator based on outlier-aware low-precision computation,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.721280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.503531Z digest=sha256:bc32ed32a0ed05f8c0fa9dbb2f7d8b1a590cbdd347dc1a65b9acd7d164d442a8

Observation 517ccf45-9858-4d36-b639-26515244142c · outbound

This paper cites With shared microexponents, a little shifting goes a long way,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration With shared microexponents, a little shifting goes a long way,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.706445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.508386Z digest=sha256:3f1c9c83b92185ee7893bd50bed23934ae030cd45aaf956fabc8b7fd4459d50a

Observation 2e0409e3-f551-44da-b734-00bd9a9c074b · outbound

This paper cites WinoGrande: An Adversarial Winograd Schema Challenge at Scale.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration WinoGrande: An Adversarial Winograd Schema Challenge at Scale

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.513379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.513379Z digest=sha256:a41ae623fde1cdccac24fffda78dca9fd3568518fc0feea3a8f2b7e2e0d8914b

Observation a457cf58-7723-45a9-9ada-8dc7e4b8e1bc · outbound

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

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.519077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.519077Z digest=sha256:07605212ccffcb2233b7448bfb4fa2cca777cf984c291fb11571bc9d5f224a07

Observation 8a532866-447d-4e22-a875-5c451cf1c976 · outbound

This paper cites Laconic deep learning inference acceleration,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Laconic deep learning inference acceleration,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.691090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.524007Z digest=sha256:15e151d75d44cb42876a81da3a03ce2343988b972448c8905d75eec6b8fc4d65

Observation 75846753-f325-412f-be10-30a2118b1a66 · outbound

This paper cites FlexGen: High-throughput generative inference of large language models with a single gpu,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration FlexGen: High-throughput generative inference of large language models with a single gpu,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.675090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.529127Z digest=sha256:28d91c35deeaa5f3e64ccd113bd55b19f4096fc9c8b97b979dbb3aad410a3e4e

Observation ced9e45b-9af1-4385-b3fd-e58832ef088a · outbound

This paper cites BitWave: Exploiting column-based bit-level sparsity for deep learning accelera- tion,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration BitWave: Exploiting column-based bit-level sparsity for deep learning accelera- tion,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.658690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.534987Z digest=sha256:ccab4cfa2b683de92e26a67d081600d31ce8173fcec708462df94504b59db027

Observation c2cd6ec7-6458-40c1-a668-fbba858da6da · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.642088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.539784Z digest=sha256:18ff6fbb979e26d9b7368cadb7fd74621d6f86ae644453a3217d4bd4e2bef696

Observation 5745f5c3-a3f5-44d5-869b-5d1251baba60 · outbound

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

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration LLaMA: Open and Efficient Foundation Language Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.544497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.544497Z digest=sha256:aff649ca61de38d01a721171219f16c32c7b320fb6198f370f9ee495d370fd3a

Observation ded4225a-9a55-406d-a7e9-75474353d037 · outbound

This paper cites Dual-side sparse tensor core,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Dual-side sparse tensor core,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.625619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.549285Z digest=sha256:0d201a84728895d1e53bfa51185db2d1db96c089bb1bb8b396c5f8035841a305

Observation ba4a69c7-6dba-4c8d-8c65-c2e4ab1dcced · outbound

This paper cites ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.554103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.554103Z digest=sha256:f9eb67c4b8d24e8e7cfac70e238be75e2a5ab0be32857601e834d116cbe081cb

Observation 6794c968-4311-451b-82f0-869deebffd6e · outbound

This paper cites HighLight: Efficient and flexible dnn acceleration with hierar- chical structured sparsity,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration HighLight: Efficient and flexible dnn acceleration with hierar- chical structured sparsity,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.609183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.559461Z digest=sha256:a9e616f47eba1f1516c828ed57c41c4abfd253a5453e21499f0a5257cfc21ed2

Observation 81bf2abb-2e50-4d24-94fe-15fb05766beb · outbound

This paper cites Quant-llm: Accelerating the serving of large language models via fp6- centric algorithm-system co-design on modern gpus,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Quant-llm: Accelerating the serving of large language models via fp6- centric algorithm-system co-design on modern gpus,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.592575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.564896Z digest=sha256:ba17b337fce0437f5c8d6ad3a3eac2d048a4348dd390ea53224e531d176a3c85

Observation e1de944c-da81-4f81-af23-5ee1f9ed88ae · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.569895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.569895Z digest=sha256:7d0bc972fcc211c0b2833fe030950df55ec7de1a0e282a1f63754364285b658f

Observation 405b02e5-3090-4efd-b4dc-2c5bd61b737b · outbound

This paper cites ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.575588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.575588Z digest=sha256:e81fbdc0bb0b93a9f5fbfcca92c1389ef6f2777581c6494dfed4bf30cf7331a4

Observation 606dc756-95a5-425d-9d03-5d5496ea83f8 · outbound

This paper cites GOBO: Quantizing attention-based nlp models for low latency and energy efficient inference,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration GOBO: Quantizing attention-based nlp models for low latency and energy efficient inference,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.576515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.581299Z digest=sha256:49d0b66a23c6b3aca8f41bfe4974f2dad372a8b7a48e8cf6af28875165e7b4bf

Observation 5148d089-888a-4569-a368-292371c62538 · outbound

This paper cites Mokey: enabling narrow fixed-point inference for out-of-the-box floating-point transformer models,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Mokey: enabling narrow fixed-point inference for out-of-the-box floating-point transformer models,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.560040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.585855Z digest=sha256:4b1fcb0b3779628850a9c502402cbbae78262e00f66df0fed5544d20cc51352d

Observation a8477de0-8536-4c58-b2bc-256281b524e6 · outbound

This paper cites HellaSwag: Can a machine really finish your sentence?.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration HellaSwag: Can a machine really finish your sentence?

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.544396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.591745Z digest=sha256:69c1500c300d5be60a0f0385eca6d1c554a1d45af6f356d84aca5640cdfb626b

Observation 6d2025b9-d479-43f7-a13b-0019896050f4 · outbound

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

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration OPT: Open Pre-trained Transformer Language Models

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.596760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.596760Z digest=sha256:02529bc3b7d58ff94bd8070b458315b4183b828ac90c6bf81e9e3d588c08e24e

Observation 7071b34d-afad-42e3-8527-b25bc1cc1da9 · outbound

This paper cites Atom: Low-bit quantization for efficient and accurate llm serving,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Atom: Low-bit quantization for efficient and accurate llm serving,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.529242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.602575Z digest=sha256:4336984d58b915bf1e5666ce864ba8c2d79bee44c9bedf779e4f64b4448f4b6d

Observation 3303371c-20b0-4fce-abae-616bb3a6d52b · outbound

This paper cites Cambricon-S: Addressing irregularity in sparse neural networks through a cooperative software/hardware approach,.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Cambricon-S: Addressing irregularity in sparse neural networks through a cooperative software/hardware approach,

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-12T18:18:05.607280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:18:05.607280Z digest=sha256:8304ebc6ec582497abf5b52d8647dec76497784426f0df1a7b831db8b9147489

Observation 36f6281a-0d00-4f7d-901a-4a42eb74069e · outbound

This paper cites an unresolved cited work.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Unresolved cited work

Reference 61

Resolution
unresolved
raw_fallback, observed 2026-08-12T18:18:06.503921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.612063Z digest=sha256:1a762db1e0b89f2bf805d323c86dc6b9ab5cdab3ab14bc5dbe92934673aa36de

Observation 78bb736f-fabf-47bd-b937-bd5f66bcb390 · outbound

This paper cites an unresolved cited work.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-12T18:18:06.489162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.616663Z digest=sha256:388728e0bf7010c8105aa8415359595121dc68260e5e65ba216cbbd2bf6a593a

Observation b7bc8668-c63e-4388-8e12-71a83e1db210 · outbound

This paper cites The quantization experiments require CUDA.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration The quantization experiments require CUDA

Reference 63

Resolution
verified exact
raw_fallback, observed 2026-08-12T18:18:05.847885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.621575Z digest=sha256:1d85752889352d5d5d2962a058df18b4e983f1bbc0444616f19cf203f93d5e80

Observation 7ef2cb9f-aa8c-4bd1-9b74-f3a7bf0e05f9 · outbound

This paper cites This can reproduce the results in Table VI and Table VIII.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration This can reproduce the results in Table VI and Table VIII

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.472199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.627026Z digest=sha256:ea8242104233903939e1d0a4f0cefd84fd1e624b5849ecedd1193bb397e1be30

Observation 4fda59cd-4fda-4ed0-83ef-5322340e1d73 · outbound

This paper cites This can reproduce the results in Fig.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration This can reproduce the results in Fig

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.455376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.632438Z digest=sha256:5fd03d5cd0cfb8126ddbf9f4f4eaaa27f2559823fc0940f9aac4b4a199173bf4

Observation ccab6954-7b06-4598-a270-b80f437b9928 · outbound

This paper cites This can reproduce the AWQ results in Table XI.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration This can reproduce the AWQ results in Table XI

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.439448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.638201Z digest=sha256:0a62f9b24d10de582dbf1ac6ad5372ef9bccda7fc0e06c09048e4f04a4ef4713

Observation 04b6b87e-c3f0-4157-a89b-5fbe856fd78c · outbound

This paper cites This can reproduce the OmniQuant results in Table XI.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration This can reproduce the OmniQuant results in Table XI

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.423721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.643669Z digest=sha256:8c8aa27e8c92f520cc257f6e29f2073c67be4d615d15b7839c034219654ca9fc

Observation c92366f4-f3f2-4e23-92e4-787655a12532 · outbound

This paper cites This can reproduce the results in Table XII.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration This can reproduce the results in Table XII

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.408812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.650161Z digest=sha256:8c1f0c987df2edd2115a9efe8ccaaedf3a44667d194ae7eff3257bb3984ec918

Observation 4507a61e-6769-434b-bce8-d90d75325cd4 · outbound

This paper cites $ cd bitmod quant $ conda activate awq−bitmod In ‘ run_exp.sh’, modify the ‘ export’ command by specifying the HuggingFace home directory, ‘ HF_HOME’, on your computer.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration $ cd bitmod quant $ conda activate awq−bitmod In ‘ run_exp.sh’, modify the ‘ export’ command by specifying the HuggingFace home directory, ‘ HF_HOME’, on your computer

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.392812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.655713Z digest=sha256:c612ba683a22778849a040e0acfe47d46fed5de3741faa6eb0ac9249fcff5338

Observation b335f717-f077-4227-9607-3bc27e81bae1 · outbound

This paper cites When enabled / disabled, it will evaluate the hardware performance of generative / discriminative tasks.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration When enabled / disabled, it will evaluate the hardware performance of generative / discriminative tasks

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.377663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T18:18:05.661738Z digest=sha256:e9283eca4a6c568f7ea244d53fbd7ff271de6cb0003592e80f4139323d3c784f

Observation 6a3efef1-2698-4d18-842d-329571d40167 · outbound

This paper cites You can compare these with the AWQ results in Table XI.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration You can compare these with the AWQ results in Table XI

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T18:18:06.362976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Unresolved cited work

Reference 72

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This paper cites You can compare these results with the SmoothQuant results in Table XII.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration You can compare these results with the SmoothQuant results in Table XII

Reference 73

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Unresolved cited work

Reference 74

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration 7 and Fig

Reference 75

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BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Unresolved cited work

Reference 76

Resolution
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Observation 97cd8206-4895-4bee-9b4d-ff9670629902 · outbound

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Reference 77

Resolution
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Source-reported events for the cited work

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Reference 78

Resolution
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Source-reported events for the cited work

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Observation 44b1c974-e20b-4d19-8cc4-f47a8ac52edb · outbound

This paper cites Available: https://zenodo.org/records/10256836.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Available: https://zenodo.org/records/10256836

Reference 2023

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

Observation f4ac93e7-a401-4204-8c39-840f13031454 · inbound

ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition cites this paper.

ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration

Reference 12

Resolution
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