APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration | Paper Citation Record

Observation 2c0249ee-648e-420a-b3af-852c7066a201 · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 1

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Observation 0699aa5c-7ee9-4474-839f-22467db1afb1 · outbound

This paper cites The Llama 3 Herd of Models.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration The Llama 3 Herd of Models

Reference 2

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Observation f1046ed7-e9ae-4b3d-8fc0-ad7eac3940fa · outbound

This paper cites DeepSeek-V3 Technical Report.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration DeepSeek-V3 Technical Report

Reference 3

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Observation 20b2debd-4947-433b-b75d-e7d68e19e71d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

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Observation de9a7b54-2bdf-4117-ad18-fa77863d036e · outbound

This paper cites Jumping nlp curves: A review of natural language processing research,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Jumping nlp curves: A review of natural language processing research,

Reference 5

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Observation 21a7223a-8da9-4462-b25f-84ba27760508 · outbound

This paper cites Scaling Laws for Neural Language Models.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Scaling Laws for Neural Language Models

Reference 6

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Observation 3a9c9fab-7b3c-42e3-a932-c722a16c76a3 · outbound

This paper cites Chateda: A large language model powered autonomous agent for eda,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Chateda: A large language model powered autonomous agent for eda,

Reference 7

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Observation 97ec05cb-66d7-42cd-be3c-4883c458397a · outbound

This paper cites Dtatrans: Leveraging dynamic token-based quantization with accuracy compensation mechanism for efficient transformer archi- tecture,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Dtatrans: Leveraging dynamic token-based quantization with accuracy compensation mechanism for efficient transformer archi- tecture,

Reference 8

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Observation 285e7a34-e16c-40b1-9d04-e41ec4daf6e7 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Qlora: Efficient finetuning of quantized llms,

Reference 9

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Observation 2a09aa32-f0e9-43b3-892a-b2d5ffa42574 · outbound

This paper cites Optq: Accurate quantization for generative pre-trained transformers,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Optq: Accurate quantization for generative pre-trained transformers,

Reference 10

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Observation 53251243-f0e0-4ebd-ac71-deab2d1b995a · outbound

This paper cites A precision-scalable risc-v dnn processor with on- device learning capability at the extreme edge,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration A precision-scalable risc-v dnn processor with on- device learning capability at the extreme edge,

Reference 11

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Observation e942cdf7-30a4-4ea2-8417-6779523368fe · outbound

This paper cites Token-scaled logit distillation for ternary weight gen- erative language models,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Token-scaled logit distillation for ternary weight gen- erative language models,

Reference 12

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Observation 7715ab1b-072b-4683-af15-09bc88d460a3 · outbound

This paper cites OneBit: Towards Extremely Low-bit Large Language Models.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration OneBit: Towards Extremely Low-bit Large Language Models

Reference 13

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Observation 34a9228e-f7c6-4f26-9ea9-ffa5b255dbc8 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 14

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Observation 2dbe31b4-d068-44b5-8d19-b14b892fff01 · outbound

This paper cites Omniquant: Omnidirectionally calibrated quantization for large language models,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Omniquant: Omnidirectionally calibrated quantization for large language models,

Reference 15

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Observation e29de1d8-be67-4cc6-a866-00e42673bd77 · outbound

This paper cites Holes: Boosting large language models efficiency with hardware-friendly lossless encoding,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Holes: Boosting large language models efficiency with hardware-friendly lossless encoding,

Reference 16

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Observation 81e59714-90dc-4fd5-b33f-69d73af0889c · outbound

This paper cites Quantization via distillation and contrastive learning,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Quantization via distillation and contrastive learning,

Reference 17

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Observation a347db84-0d13-4c1c-bfb1-733796f494e4 · outbound

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

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Atom: Low-bit quantization for efficient and accurate llm serving,

Reference 18

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Observation 78e738c1-ebaa-4270-bd8e-f0f32a7b3542 · outbound

This paper cites Nvidia a100 tensor core gpu: Performance and innovation,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Nvidia a100 tensor core gpu: Performance and innovation,

Reference 19

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Observation 8b2eb790-87f0-4345-95e9-5dad8cc06a8c · outbound

This paper cites Rtx on—the nvidia turing gpu,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Rtx on—the nvidia turing gpu,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:49.365835Z

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Observation 61402a7a-7864-4591-806f-256035d61ed8 · outbound

This paper cites Dissecting tensor cores via microbenchmarks: Latency, throughput and numeric behaviors,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Dissecting tensor cores via microbenchmarks: Latency, throughput and numeric behaviors,

Reference 21

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Observation f6e2c64e-11de-4867-be05-59d2e3d3b060 · outbound

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

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 22

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Observation 02687371-529f-495d-bdff-b1680de5b07c · outbound

This paper cites G-blastn: accelerating nucleotide alignment by graphics processors,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration G-blastn: accelerating nucleotide alignment by graphics processors,

Reference 23

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Observation 972eb02e-45bf-4945-8ff3-c7891e9db510 · outbound

This paper cites Accelerating performance of gpu-based workloads using cxl,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Accelerating performance of gpu-based workloads using cxl,

Reference 24

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Observation 0d767571-b9e6-4997-b49f-ecf36bb09708 · outbound

This paper cites Superneurons: Dynamic gpu memory management for training deep neural networks,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Superneurons: Dynamic gpu memory management for training deep neural networks,

Reference 25

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Observation 862a8792-8b7c-43a0-9836-96f2dbbd69e9 · outbound

This paper cites Gpt3. int8 (): 8-bit matrix multiplication for trans- formers at scale,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Gpt3. int8 (): 8-bit matrix multiplication for trans- formers at scale,

Reference 26

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Observation a7457989-dce0-4e7f-9a58-7ff4d12c6b58 · outbound

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

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Quant-llm: Accelerating the serving of large language models via fp6-centric algorithm-system co-design on modern gpus,

Reference 27

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Observation caa6ff66-940d-461e-992b-d09fa11ae585 · outbound

This paper cites Tsm2x: High-performance tall-and-skinny matrix– matrix multiplication on gpus,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Tsm2x: High-performance tall-and-skinny matrix– matrix multiplication on gpus,

Reference 28

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Observation 73a8173f-ae2d-4e27-a9a0-f835c17507ed · outbound

This paper cites Stream-k: Work-centric parallel decomposition for dense matrix-matrix multiplication on the gpu,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Stream-k: Work-centric parallel decomposition for dense matrix-matrix multiplication on the gpu,

Reference 29

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Observation efedc03e-0a91-4e01-9a05-fab1cfa26ab3 · outbound

This paper cites Warp-aware adaptive energy efficiency calibration for multi-gpu systems,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Warp-aware adaptive energy efficiency calibration for multi-gpu systems,

Reference 30

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Observation c55015c0-a37a-4ba3-991e-9e6c536392ab · outbound

This paper cites Dg-replace: A dataflow-driven gpu-accelerated an- alytical global placement framework for machine learning accelerators,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Dg-replace: A dataflow-driven gpu-accelerated an- alytical global placement framework for machine learning accelerators,

Reference 31

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Observation 6a408321-96c9-44fb-8927-41f1b0f5f099 · outbound

This paper cites Enabling efficient sparse multiplications on gpus with heuristic adaptability,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Enabling efficient sparse multiplications on gpus with heuristic adaptability,

Reference 32

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Observation f204c8cb-b910-4361-9f64-5ac40004a4c5 · outbound

This paper cites Bstc: A novel binarized-soft-tensor-core design for acceler- ating bit-based approximated neural nets,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Bstc: A novel binarized-soft-tensor-core design for acceler- ating bit-based approximated neural nets,

Reference 33

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Observation 1f0e1a3d-b951-4eec-9265-5029184e20fe · outbound

This paper cites Accelerating binarized neural networks via bit-tensor-cores in turing gpus,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Accelerating binarized neural networks via bit-tensor-cores in turing gpus,

Reference 34

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Observation a7657730-372d-442d-9efe-85a35fbaa7d5 · outbound

This paper cites Demystifying the nvidia ampere architecture through microbenchmarking and instruction-level analysis,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Demystifying the nvidia ampere architecture through microbenchmarking and instruction-level analysis,

Reference 35

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Observation ddd27145-4aa4-4b4e-bd39-8a1262afe34b · outbound

This paper cites Dissecting the NVidia Turing T4 GPU via Microbenchmarking.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Dissecting the NVidia Turing T4 GPU via Microbenchmarking

Reference 36

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Observation c1cfc1ed-d322-48d7-9a5a-b4352db32e67 · outbound

This paper cites Gtco: Graph and tensor co-design for transformer-based image recognition on tensor cores,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Gtco: Graph and tensor co-design for transformer-based image recognition on tensor cores,

Reference 38

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Observation 10a85451-0f7f-44a6-b720-0855114b9c2a · outbound

This paper cites Reducing shared memory footprint to leverage high throughput on tensor cores and its flexible api extension library,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Reducing shared memory footprint to leverage high throughput on tensor cores and its flexible api extension library,

Reference 39

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Observation d3ba3b1d-64d2-4aac-80b0-1c17207ef002 · outbound

This paper cites Tc-gnn: Bridging sparse gnn computation and dense tensor cores on gpus,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Tc-gnn: Bridging sparse gnn computation and dense tensor cores on gpus,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:43.542363Z

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Observation bc41d50c-520f-4568-b483-fee0d23a90d6 · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration A Survey on Efficient Inference for Large Language Models

Reference 41

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Observation 936e10d2-b8f9-432d-b9d3-406727c0d6b3 · outbound

This paper cites Transformer tricks: Precomputing the first layer.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Transformer tricks: Precomputing the first layer

Reference 42

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Observation 20028b8c-8e9b-4be8-9aad-7f086a53eca0 · outbound

This paper cites Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

Reference 43

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Observation 0384a01f-6700-410c-b846-6077e1f05f29 · outbound

This paper cites Efficiently scaling transformer inference,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Efficiently scaling transformer inference,

Reference 44

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Observation 50dbda44-73ad-4c8c-b464-a047058de65d · outbound

This paper cites LLM Inference Unveiled: Survey and Roofline Model Insights.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration LLM Inference Unveiled: Survey and Roofline Model Insights

Reference 45

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Observation 23f16808-6923-48eb-9bd2-dbfd81312a1f · outbound

This paper cites Squeezellm: Dense-and-sparse quantization,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Squeezellm: Dense-and-sparse quantization,

Reference 46

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Observation 602f8122-5394-423d-9363-362753a53a0f · outbound

This paper cites Quantsr: accurate low-bit quantization for efficient image super-resolution,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Quantsr: accurate low-bit quantization for efficient image super-resolution,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:42.735145Z

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Observation 8c9c4e4d-e22e-4f6a-a269-72f65297aff9 · outbound

This paper cites Accurate lora-finetuning quantization of llms via infor- mation retention,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Accurate lora-finetuning quantization of llms via infor- mation retention,

Reference 48

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Observation aa6adcfe-69e2-4d00-bdd2-aa522c72ac1a · outbound

This paper cites Bimatting: Efficient video matting via binarization,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Bimatting: Efficient video matting via binarization,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:42.173545Z

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Observation dc0e5c26-be37-45c6-a541-59cffa191625 · outbound

This paper cites Bibert: Accurate fully binarized bert,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Bibert: Accurate fully binarized bert,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:41.854183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6b77b327-62a3-4095-bb3d-bf53d3e02ad0 · outbound

This paper cites Bebert: Efficient and robust binary ensemble bert,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Bebert: Efficient and robust binary ensemble bert,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:41.556430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation febe2237-c4f7-4ba0-bdb7-eb4f062c9e7f · outbound

This paper cites Anda: Unlocking efficient llm inference with a variable- length grouped activation data format,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Anda: Unlocking efficient llm inference with a variable- length grouped activation data format,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:41.285241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e934cee2-b33f-4156-9766-e77fc31f79e5 · outbound

This paper cites Binaryconnect: Training deep neural networks with binary weights during propagations,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Binaryconnect: Training deep neural networks with binary weights during propagations,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:41.038528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 32badd89-5357-4eb1-bf96-f3abad82b614 · outbound

This paper cites Apnn-tc: Accelerating arbitrary precision neural net- works on ampere gpu tensor cores,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Apnn-tc: Accelerating arbitrary precision neural net- works on ampere gpu tensor cores,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:40.746968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 85c7506c-6611-476b-bd56-2eafefebde65 · outbound

This paper cites O3bnn: An out-of-order architecture for high- performance binarized neural network inference with fine-grained prun- ing,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration O3bnn: An out-of-order architecture for high- performance binarized neural network inference with fine-grained prun- ing,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:40.550324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2e159bb0-57f2-4c08-8b4a-a89501d8ca9f · outbound

This paper cites O3bnn-r: An out-of-order architecture for high- performance and regularized bnn inference,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration O3bnn-r: An out-of-order architecture for high- performance and regularized bnn inference,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:40.290730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a82f4c78-4e48-4753-9e7d-fca5a7664637 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:36.820892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f7676a-04bf-4c3b-9020-9fc997bb10ac · outbound

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

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Energy-efficient neural network accelerator based on outlier-aware low-precision computation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:40.054971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation beb297e3-6573-4407-8cf0-a383adc8e25b · outbound

This paper cites Haq: Hardware-aware automated quantization with mixed precision,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Haq: Hardware-aware automated quantization with mixed precision,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:39.822948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f35f174-06ed-4ec5-90b4-044f816a4ede · outbound

This paper cites Lq-nets: Learned quantization for highly accurate and compact deep neural networks,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Lq-nets: Learned quantization for highly accurate and compact deep neural networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:39.574486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed54c9ca-a242-4b44-ad33-9e83c77fd0f8 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:37.317001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b701fa-203f-4860-86af-cb433ba3e196 · outbound

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

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:37.480444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4a7e274-a1e0-4630-8f06-594cc0e859fa · outbound

This paper cites CUTLASS,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration CUTLASS,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:39.296976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b8e1fab-3214-4f98-ab05-99f3338c3ecd · outbound

This paper cites Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:03:38.709223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 647c40c9-853c-4d34-83ae-be0dbcf3c981 · outbound

This paper cites Benchmarking and dissecting the nvidia hopper gpu architecture,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Benchmarking and dissecting the nvidia hopper gpu architecture,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:44.427189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 218ea8a5-470c-47c8-9151-ce529186440c · outbound

This paper cites Qwen2.5 Technical Report.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Qwen2.5 Technical Report

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:37.961793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23525f4e-8c54-465c-9ef4-0e17cad9dccd · outbound

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

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration OPT: Open Pre-trained Transformer Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:38.057130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 372fa1e2-d486-4352-ad8f-e8c613a2b8e1 · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:38.218549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 831a1e11-38ba-443b-8d03-a72fe0122d8c · outbound

This paper cites Quarot: Outlier-free 4-bit inference in rotated llms,.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Quarot: Outlier-free 4-bit inference in rotated llms,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:03:39.050426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 90c16b91-f1f3-4421-860f-f5104b84fcdd · outbound

This paper cites Pointer Sentinel Mixture Models.

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration Pointer Sentinel Mixture Models

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T16:03:38.453321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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