Paper Citation Record · LEDGER

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator

As of 22 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 1 inbound Pith citation observation for arXiv:2501.10658.

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

pith.paper-citation-record.v1
2501.10658 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

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measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:00:22.893297Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

61 of 61 outbound references displayed

verified exact4
verified fuzzy19
unresolved36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dde9fbb6-d633-4f91-85d0-43d7555a4b07 · outbound

This paper cites Pqa: Exploring the potential of product quantization in dnn hardware acceleration,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Pqa: Exploring the potential of product quantization in dnn hardware acceleration,

Reference 1

Resolution: verified exact; doi, observed 2026-08-10T19:08:08.394053Z

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Observation 945d3ae2-6be6-4cbb-97b5-737f22d6a72f · outbound

This paper cites Hardware approximate techniques for deep neural network accelerators: A survey,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Hardware approximate techniques for deep neural network accelerators: A survey,

Reference 2

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Observation bbc4cb98-bf31-46c3-948a-67510288076e · outbound

This paper cites Chisel: constructing hardware in a scala embedded language,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Chisel: constructing hardware in a scala embedded language,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.836405Z

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Observation 9ab3a886-df53-48af-9201-8a805059bf48 · outbound

This paper cites Multiplying matrices without multiplying,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Multiplying matrices without multiplying,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.821872Z

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Observation 783eddc8-83de-4095-86f1-d8b7ec4e726b · outbound

This paper cites RTX on - the NVIDIA turing GPU,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator RTX on - the NVIDIA turing GPU,

Reference 5

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Observation c91d9d9d-71ef-4756-860f-83f49123470e · outbound

This paper cites Deepburning-seg: Generating DNN accelerators of segment-grained pipeline architecture,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Deepburning-seg: Generating DNN accelerators of segment-grained pipeline architecture,

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-10T19:08:10.265703Z

Source-reported events for the cited work

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Observation c7254e08-8015-43ef-8ad3-5f6c41868bbd · outbound

This paper cites QuIP: 2-bit quantization of large language models with guarantees,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator QuIP: 2-bit quantization of large language models with guarantees,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.807946Z

Source-reported events for the cited work

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Observation d08fa562-8aa5-41b1-b13d-f3f962dbb987 · outbound

This paper cites NVIDIA hopper H100 GPU: scaling performance,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator NVIDIA hopper H100 GPU: scaling performance,

Reference 8

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Source-reported events for the cited work

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Observation 18d4721f-2d83-4053-9ae7-c8e9d6285243 · outbound

This paper cites NVIDIA A100 tensor core GPU: performance and innovation,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator NVIDIA A100 tensor core GPU: performance and innovation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:07.769457Z

Source-reported events for the cited work

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Observation 46756d9c-9a17-49be-a18c-214454343e9b · outbound

This paper cites Using vector quantization for image processing,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Using vector quantization for image processing,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.793785Z

Source-reported events for the cited work

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Observation 9b678fd2-7b24-405e-852e-d641dc64c74e · outbound

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

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:07.780471Z

Source-reported events for the cited work

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Observation 16d6186e-d5f9-4e56-934d-2c7bb7a96dfd · outbound

This paper cites The accelerator wall: Limits of chip specialization,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator The accelerator wall: Limits of chip specialization,

Reference 12

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Observation 793fbf82-31c7-4e1e-8895-ccbe81c2d5cc · outbound

This paper cites Optimized product quantization for approximate nearest neighbor search,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Optimized product quantization for approximate nearest neighbor search,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:07.795884Z

Source-reported events for the cited work

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Observation fcec88cd-2518-4c13-bd8d-1e70ced5ed30 · outbound

This paper cites Optimized product quantization,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Optimized product quantization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.646686Z

Source-reported events for the cited work

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Observation dbc75e92-730d-421e-9c05-40f26a291deb · outbound

This paper cites Gemmini: Enabling systematic deep- learning architecture evaluation via full-stack integration,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Gemmini: Enabling systematic deep- learning architecture evaluation via full-stack integration,

Reference 15

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Observation 4682dfed-12d2-464f-9701-188e401d7273 · outbound

This paper cites Vector quantization,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Vector quantization,

Reference 16

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Source-reported events for the cited work

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Observation 9f359ad4-4431-455c-ad50-209be68b37b5 · outbound

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

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Ant: Exploiting adaptive numerical data type for low-bit deep neural network quantization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.613050Z

Source-reported events for the cited work

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Observation 07d1ff8b-fa36-4731-b148-6b0a2e64bac8 · outbound

This paper cites NNPIM: A processing in-memory architecture for neural network acceleration,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator NNPIM: A processing in-memory architecture for neural network acceleration,

Reference 19

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Observation 61babdbf-e420-48c3-be3f-707aad83937d · outbound

This paper cites ELSA: hardware-software co-design for efficient, lightweight self- attention mechanism in neural networks,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator ELSA: hardware-software co-design for efficient, lightweight self- attention mechanism in neural networks,

Reference 20

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Source-reported events for the cited work

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Observation f3712130-205e-4ee7-b700-c2e737a97e00 · outbound

This paper cites Approximate computing: An emerging paradigm for energy-efficient design,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Approximate computing: An emerging paradigm for energy-efficient design,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.591967Z

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Observation 6aea3903-ec86-4658-a4a0-ce23f50477a1 · outbound

This paper cites EIE: efficient inference engine on compressed deep neural network,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator EIE: efficient inference engine on compressed deep neural network,

Reference 22

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Observation ee4ad6f5-7bf9-4ae6-b0d3-149fa17d5785 · outbound

This paper cites Limits to the energy efficiency of cmos microprocessors,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Limits to the energy efficiency of cmos microprocessors,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.575332Z

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Observation 1845a48b-614d-489c-941b-9610a8d040cd · outbound

This paper cites Training Compute-Optimal Large Language Models.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Training Compute-Optimal Large Language Models

Reference 24

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Observation 038586c4-963a-4aa8-8c00-199abc84f01b · outbound

This paper cites RAPIDNN: In-Memory Deep Neural Network Acceleration Framework.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator RAPIDNN: In-Memory Deep Neural Network Acceleration Framework

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:08:09.631026Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d0595540-0190-4271-9341-1cf654343799 · outbound

This paper cites TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:08:08.293056Z

Source-reported events for the cited work

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Observation a26c901e-d9a5-4fce-a306-0d6e9c499026 · outbound

This paper cites TPU v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator TPU v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings,

Reference 27

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Source-reported events for the cited work

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Observation 2afc66ba-96b0-462c-9154-7931e7cbda62 · outbound

This paper cites In-datacenter performance analysis of a tensor processing unit,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator In-datacenter performance analysis of a tensor processing unit,

Reference 28

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Observation 87c11917-38dd-4b71-82d8-f2f341604ecb · outbound

This paper cites Product quantization for nearest neighbor search,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Product quantization for nearest neighbor search,

Reference 29

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Source-reported events for the cited work

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Observation 0515ce70-0426-461b-ab6c-bd9d4c477f36 · outbound

This paper cites Scaling Laws for Neural Language Models.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Scaling Laws for Neural Language Models

Reference 30

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Source-reported events for the cited work

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Observation 126d5574-bd54-4211-93b8-b6c73934c7cd · outbound

This paper cites Irreversibility and heat generation in the computing process,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Irreversibility and heat generation in the computing process,

Reference 31

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Observation bbce58c6-aa07-4017-8078-cba08ea1f1c9 · outbound

This paper cites Pim-dl: Expanding the applicability of commodity dram-pims for deep learning via algorithm-system co-optimization,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Pim-dl: Expanding the applicability of commodity dram-pims for deep learning via algorithm-system co-optimization,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.538600Z

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Observation 3ff823bc-901a-4202-b945-66d37befa08f · outbound

This paper cites Boosting mobile CNN inference through semantic memory,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Boosting mobile CNN inference through semantic memory,

Reference 33

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Source-reported events for the cited work

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Observation 7e70fbcb-1e67-459a-97cc-1ab0134b9100 · outbound

This paper cites RRAM-DNN: an RRAM and model-compression empowered all-weights-on-chip DNN accelerator,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator RRAM-DNN: an RRAM and model-compression empowered all-weights-on-chip DNN accelerator,

Reference 34

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Source-reported events for the cited work

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Observation 6ac19495-ff49-4400-a00e-ab74d6173202 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 35

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Observation be3c4794-779c-4a50-b11c-7a24aa41e8f1 · outbound

This paper cites LLM-FP4: 4-bit floating-point quantized transformers,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator LLM-FP4: 4-bit floating-point quantized transformers,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.521015Z

Source-reported events for the cited work

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Observation 9132cc3c-5d72-4be5-9d55-e4a14c08dcfd · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 37

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Source-reported events for the cited work

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Observation d842961a-9f1c-494f-bace-2b4ea4b5e1fe · outbound

This paper cites Vector quantization in speech coding,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Vector quantization in speech coding,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.505268Z

Source-reported events for the cited work

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Observation 752d69fb-7f16-4d81-9aa1-58bcb390921e · outbound

This paper cites FP8 Formats for Deep Learning.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator FP8 Formats for Deep Learning

Reference 40

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Source-reported events for the cited work

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Observation 09f60f27-6cc7-4046-bed8-f3959c9cdf38 · outbound

This paper cites Energy-efficient convolutional neural networks via recurrent data reuse,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Energy-efficient convolutional neural networks via recurrent data reuse,

Reference 41

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Observation 91425202-83e9-4ab6-81ba-451124866bb2 · outbound

This paper cites Evoapprox8b: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Evoapprox8b: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.486576Z

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Observation 7559f981-faeb-4cdf-b3d9-7aad4f53aab6 · outbound

This paper cites Memory-Centric Computing.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Memory-Centric Computing

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:07.973238Z

Source-reported events for the cited work

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Observation c2cab699-1c35-48b8-a9e7-c13c56cbc549 · outbound

This paper cites Nvdla open source hardware performance.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Nvdla open source hardware performance

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.470316Z

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Observation bcb94b94-b06a-4006-a3ac-334198fc5ff3 · outbound

This paper cites Nvidia deep learning accelerator.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Nvidia deep learning accelerator

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.452041Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ce3b791-eca7-433b-be86-81b764246440 · outbound

This paper cites (2024) Nvidia dgx b200 datasheet.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator (2024) Nvidia dgx b200 datasheet

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.434661Z

Source-reported events for the cited work

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Observation a44c2e24-f800-4e0f-9873-dd6cd074b41a · outbound

This paper cites SCNN: an accelerator for compressed-sparse convolutional neural networks,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator SCNN: an accelerator for compressed-sparse convolutional neural networks,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.001932Z

Source-reported events for the cited work

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Observation 9d1d1dd8-d926-4de9-ad7e-b567fac39aba · outbound

This paper cites LUT-GEMM: quantized matrix multiplication based on luts for efficient inference in large- scale generative language models,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator LUT-GEMM: quantized matrix multiplication based on luts for efficient inference in large- scale generative language models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.421304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8d789c1a-f89d-4a41-a8e7-66b87787142a · outbound

This paper cites FACT: ffn-attention co-optimized transformer architecture with eager correlation prediction,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator FACT: ffn-attention co-optimized transformer architecture with eager correlation prediction,

Reference 49

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Observation e074848e-62cb-43ca-8783-0029119ad8af · outbound

This paper cites PECAN: A product-quantized content addressable memory network,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator PECAN: A product-quantized content addressable memory network,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.017830Z

Source-reported events for the cited work

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Observation 93570ab9-6cf5-4a8f-a4d0-fed8eee6a97b · outbound

This paper cites Computation reuse in dnns by exploiting input similarity,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Computation reuse in dnns by exploiting input similarity,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.022431Z

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Observation 2cdcfb90-4557-4fc7-be28-93cc6995a168 · outbound

This paper cites Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.027112Z

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Observation 9b500f3b-6d8c-477e-877f-309bda87e8bc · outbound

This paper cites Softermax: Hardware/software co-design of an efficient softmax for transformers,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Softermax: Hardware/software co-design of an efficient softmax for transformers,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.032545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34abd2e0-41a2-4c6e-9026-7e80254b06ab · outbound

This paper cites Scaling equations for the accurate prediction of CMOS device performance from 180 nm to 7 nm,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Scaling equations for the accurate prediction of CMOS device performance from 180 nm to 7 nm,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.038426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9599c695-f274-447c-aedc-86d642f01950 · outbound

This paper cites LUT-NN: empower efficient neural network inference with centroid learning and table lookup,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator LUT-NN: empower efficient neural network inference with centroid learning and table lookup,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.043432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf9e661f-2d46-4e89-8a27-1cf8ec9f2291 · outbound

This paper cites Weight-oriented approximation for energy-efficient neural network inference accelerators,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Weight-oriented approximation for energy-efficient neural network inference accelerators,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.404860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9a70b770-46a9-4cc0-854a-c80316aa0560 · outbound

This paper cites Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.387874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d656427d-278e-486d-8a91-0e9c0e7afe32 · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language understanding,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator GLUE: A multi-task benchmark and analysis platform for natural language understanding,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:08:10.373313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd5792ab-8f03-4ca8-816f-b9a23f5f0670 · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.073599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d89cb65-52f9-4215-b743-6cbd1c109726 · outbound

This paper cites Learnable lookup table for neural network quantization,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Learnable lookup table for neural network quantization,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.079050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6924ed70-2c7d-43c5-9098-8eba4c1818c3 · outbound

This paper cites Nn-lut: Neural approximation of non-linear operations for efficient transformer inference,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Nn-lut: Neural approximation of non-linear operations for efficient transformer inference,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.083496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f1218e3-960b-49f8-89f6-3f7bbf203cd4 · outbound

This paper cites Dnnbuilder: an automated tool for building high-performance DNN hardware accelerators for fpgas,.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Dnnbuilder: an automated tool for building high-performance DNN hardware accelerators for fpgas,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:08.088488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21ea5814-1250-4946-a557-02e033d83060 · outbound

This paper cites Memory-Centric Computing.

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator Memory-Centric Computing

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T19:08:07.978735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 28d25403-e5eb-461c-858a-f3f9b86db23f · inbound

MCBP: A Memory-Compute Efficient LLM Inference Accelerator Leveraging Bit-Slice-enabled Sparsity and Repetitiveness cites this paper.

MCBP: A Memory-Compute Efficient LLM Inference Accelerator Leveraging Bit-Slice-enabled Sparsity and Repetitiveness LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T18:00:22.893297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.