MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices | Paper Citation Record

Observation 08ddaea7-4bee-4635-8789-45e8964ad17f · outbound

This paper cites https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-803/best-practices/index.html.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-803/best-practices/index.html

Reference 1

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Observation a4970715-898a-4066-a788-ea774ac0a9a8 · outbound

This paper cites https://developer.apple.com/documentation/accelerate/bnns, a.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices https://developer.apple.com/documentation/accelerate/bnns, a

Reference 2

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This paper cites https://apple.github.io/coremltools/docs-guides/source/opt-palettization-overview.html, b.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices https://apple.github.io/coremltools/docs-guides/source/opt-palettization-overview.html, b

Reference 3

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Observation bdab1616-2a0f-4fa2-82e6-baf3a1b60459 · outbound

This paper cites https://developer.apple.com/documentation/metalperformanceshadersgraph, c.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices https://developer.apple.com/documentation/metalperformanceshadersgraph, c

Reference 4

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Observation 15003672-7012-4a93-acc2-1847b4cba90a · outbound

This paper cites an unresolved cited work.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Unresolved cited work

Reference 5

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Observation d5088739-c155-403a-b9ec-bc3906eb6fd3 · outbound

This paper cites https://www.tensorflow.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices https://www.tensorflow

Reference 6

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Observation cb93ec47-6c47-45ec-ba94-95a0c8ff885d · outbound

This paper cites Y., Rajbhandari, S., Awan, A.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Y., Rajbhandari, S., Awan, A

Reference 7

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Observation 57db820d-51e3-4f8d-8de9-c98fe27b3422 · outbound

This paper cites B., Del Sozzo, E., Akkas, A., Zhang, Y., Suriana, P., Kamil, S., and Amarasinghe, S.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices B., Del Sozzo, E., Akkas, A., Zhang, Y., Suriana, P., Kamil, S., and Amarasinghe, S

Reference 8

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Observation dc2bee9f-c6ff-4cb5-9eed-eed8944db39f · outbound

This paper cites \ TVM \ : An automated \ End-to-End \ optimizing compiler for deep learning.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices \ TVM \ : An automated \ End-to-End \ optimizing compiler for deep learning

Reference 9

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Observation 43f3e696-0448-442b-8e1a-b2c1b6a68453 · outbound

This paper cites Learning to optimize tensor programs.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Learning to optimize tensor programs

Reference 10

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Observation 55476913-9260-4e8d-8641-16e3905322c1 · outbound

This paper cites Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices

Reference 11

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Observation 211ed60a-b480-419d-942b-dcae66d7d2f5 · outbound

This paper cites DKM: Differentiable K-Means Clustering Layer for Neural Network Compression.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices DKM: Differentiable K-Means Clustering Layer for Neural Network Compression

Reference 12

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Observation b1cd0939-2d2c-4056-9593-6f0adb04c06c · outbound

This paper cites KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation

Reference 13

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Observation f7170fa5-29ef-4020-b071-a53080d6c696 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 14

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Observation 657bbd5a-c650-4f11-a93a-19b054cab6f9 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 15

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Observation d7eac942-454e-4b2b-ab0f-2ea4d77b8de9 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 16

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Observation c218a399-5fdd-41d8-ba87-b2981d6d77f0 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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Observation 1085b971-9779-4f53-872a-6ea70244359e · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Scaling rectified flow transformers for high-resolution image synthesis

Reference 18

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Observation 4cb0e3f2-cc72-49a5-8545-a3c623e882ff · outbound

This paper cites Videoagent: A memory-augmented multimodal agent for video understanding.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Videoagent: A memory-augmented multimodal agent for video understanding

Reference 19

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Observation 422ae86d-9f4f-4d19-86d5-a3a043454b43 · outbound

This paper cites A., Yang, Y., Sajjad, H., Nakov, P., Chen, D., and Winslett, M.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices A., Yang, Y., Sajjad, H., Nakov, P., Chen, D., and Winslett, M

Reference 20

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Observation f1c2b330-65ae-48bb-9f1f-8e8f0f4838ab · outbound

This paper cites Collective loop fusion for array contraction.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Collective loop fusion for array contraction

Reference 21

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Observation 74701642-78c7-4236-8b3d-53d277a454e8 · outbound

This paper cites Improving alignment of dialogue agents via targeted human judgements.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Improving alignment of dialogue agents via targeted human judgements

Reference 22

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Observation 2e33714f-b51c-4fb2-993d-70bc49c617ef · outbound

This paper cites Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss

Reference 23

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Observation 615c5762-3583-416c-8876-20a484ea1b5b · outbound

This paper cites FlashDecoding++: Faster Large Language Model Inference on GPUs.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices FlashDecoding++: Faster Large Language Model Inference on GPUs

Reference 24

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This paper cites Knowledge diffusion for distillation.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Knowledge diffusion for distillation

Reference 25

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Observation e59812d8-4cad-4ae5-b3e9-b6405a920dc5 · outbound

This paper cites Data movement is all you need: A case study on optimizing transformers.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Data movement is all you need: A case study on optimizing transformers

Reference 26

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Observation 6a5d504d-efc5-4178-800a-4a1300b2c002 · outbound

This paper cites P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al

Reference 27

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This paper cites P., Yoon, D.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices P., Yoon, D

Reference 28

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This paper cites Flat: An optimized dataflow for mitigating attention bottlenecks.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Flat: An optimized dataflow for mitigating attention bottlenecks

Reference 29

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Observation 070363a4-5661-4352-a63a-91533cb51988 · outbound

This paper cites Scaling Laws for Neural Language Models.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Scaling Laws for Neural Language Models

Reference 30

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MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Unresolved cited work

Reference 31

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MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Reformer: The Efficient Transformer

Reference 32

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This paper cites The tensor algebra compiler.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices The tensor algebra compiler

Reference 33

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This paper cites Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable interconnects.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Maeri: Enabling flexible dataflow mapping over dnn accelerators via reconfigurable interconnects

Reference 34

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This paper cites H., Gonzalez, J., Zhang, H., and Stoica, I.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices H., Gonzalez, J., Zhang, H., and Stoica, I

Reference 35

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MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Cross-lingual Language Model Pretraining

Reference 36

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This paper cites Multimodal foundation models: From specialists to general-purpose assistants.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Multimodal foundation models: From specialists to general-purpose assistants

Reference 37

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This paper cites onednn graph compiler: A hybrid approach for high-performance deep learning compilation.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices onednn graph compiler: A hybrid approach for high-performance deep learning compilation

Reference 38

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This paper cites Q-vit: Accurate and fully quantized low-bit vision transformer.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Q-vit: Accurate and fully quantized low-bit vision transformer

Reference 39

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Observation 8b3bfa95-725f-4604-af59-7f418b843ada · outbound

This paper cites and Gu, Q.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices and Gu, Q

Reference 40

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Observation 387af77b-2df8-4b2d-accf-3a4946d51a77 · outbound

This paper cites Davinci: A scalable architecture for neural network computing.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Davinci: A scalable architecture for neural network computing

Reference 41

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This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 42

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This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 43

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This paper cites Post-training quantization for vision transformer.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Post-training quantization for vision transformer

Reference 44

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Observation 0ed8ecc8-2895-4b75-8eb6-691ee52ec6bd · outbound

This paper cites Tprune: Efficient transformer pruning for mobile devices.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Tprune: Efficient transformer pruning for mobile devices

Reference 45

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Observation f8ee8da5-b38d-4077-8b0b-383e1b4b42de · outbound

This paper cites OpenELM: An Efficient Language Model Family with Open Training and Inference Framework.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

Reference 46

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Observation 4776660d-adb1-4ad3-975a-da4b14a28e88 · outbound

This paper cites Defines: Enabling fast exploration of the depth-first scheduling space for dnn accelerators through analytical modeling.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Defines: Enabling fast exploration of the depth-first scheduling space for dnn accelerators through analytical modeling

Reference 47

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Observation 6f9af71f-4511-4291-833b-d5d342481dd3 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 48

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Observation 9e5f5e38-9f5e-4c17-91e7-deeb4abb448b · outbound

This paper cites O., Pellauer, M., Emer, J.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices O., Pellauer, M., Emer, J

Reference 49

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Observation 80725d92-7ce5-45de-855b-9582a78e3831 · outbound

This paper cites Dnnfusion: accelerating deep neural networks execution with advanced operator fusion.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Dnnfusion: accelerating deep neural networks execution with advanced operator fusion

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.782306Z

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Observation 5bfb9de1-0142-4b59-b842-07954cde34b8 · outbound

This paper cites Training language models to follow instructions with human feedback.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Training language models to follow instructions with human feedback

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.064414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18194aa1-6223-4ac5-b4ad-e169f3f075fd · outbound

This paper cites S., Chen, Y.-H., Ying, V.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices S., Chen, Y.-H., Ying, V

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.738941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 87b29825-d987-4b8c-bc11-51cff97aba81 · outbound

This paper cites Splitwise: Efficient generative llm inference using phase splitting.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Splitwise: Efficient generative llm inference using phase splitting

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.713331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation add3a1c1-9f93-419c-b611-856cb36133aa · outbound

This paper cites and Xie, S.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices and Xie, S

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.080119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c14337a-f0f9-4d91-968c-5edbfc34738b · outbound

This paper cites Accelerating transformer-based deep learning models on fpgas using column balanced block pruning.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Accelerating transformer-based deep learning models on fpgas using column balanced block pruning

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.679219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation abd6822c-fb4f-494f-ba47-c296261c1175 · outbound

This paper cites Sensimix: Sensitivity-aware 8-bit index & 1-bit value mixed precision quantization for bert compression.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Sensimix: Sensitivity-aware 8-bit index & 1-bit value mixed precision quantization for bert compression

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.659045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2872df2a-25f8-4999-9c0d-a0d25d4d3bc3 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices DreamFusion: Text-to-3D using 2D Diffusion

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.095869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c136a145-3aa9-40d2-895f-ea5a9980a11a · outbound

This paper cites Improving language understanding by generative pre-training.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Improving language understanding by generative pre-training

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.101666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f0c9c46-f664-4789-b55c-77084f7ee513 · outbound

This paper cites an unresolved cited work.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.106594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81203344-da31-447d-a4e6-1bf7e9f1b1e9 · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.111284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 914bfc6f-707e-4d7c-9b13-2b7c03fa503a · outbound

This paper cites FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.115971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e132c2de-bb0a-4801-8867-756b16a441cf · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.120751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 193681a3-7ed0-4996-90b7-2541e155a979 · outbound

This paper cites Patient Knowledge Distillation for BERT Model Compression.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Patient Knowledge Distillation for BERT Model Compression

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.125648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 664c87d5-e70c-47b7-a61e-c85db9a2556a · outbound

This paper cites Improving the efficiency of transformers for resource-constrained devices.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Improving the efficiency of transformers for resource-constrained devices

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.593717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e2cb3bf3-da56-4378-aebe-8a4bd7979bea · outbound

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

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices LLaMA: Open and Efficient Foundation Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.135423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e10fc1db-b711-458e-92cd-fb706b1e38a6 · outbound

This paper cites N., Kaiser, ., and Polosukhin, I.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices N., Kaiser, ., and Polosukhin, I

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.140419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 306620eb-8751-4452-8a76-eadb6a7025a0 · outbound

This paper cites Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.145702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4417ca96-4b92-4ad2-8bed-f169a120af87 · outbound

This paper cites C., Venkataramani, S., Sen, S., Chen, C.-Y., El Maghraoui, K., Srinivasan, V.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices C., Venkataramani, S., Sen, S., Chen, C.-Y., El Maghraoui, K., Srinivasan, V

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.555143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a0a64d52-cdb4-4bfd-a69e-d4999e7ed5dd · outbound

This paper cites Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-12T16:20:03.324238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c5423b7d-d1a1-44ea-aa1f-3ca1828e13ba · outbound

This paper cites MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.161573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f84c3fab-eeba-4ecc-ab12-502be72f0fb8 · outbound

This paper cites Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.535907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 08a15981-5a58-4b34-82d3-c8c6f209b796 · outbound

This paper cites N., Emer, J.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices N., Emer, J

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.515715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d62d813a-88ba-48c8-b259-283f78199d63 · outbound

This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.175603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7925b971-d851-4c7e-9663-1c097be4b72d · outbound

This paper cites Boost vision transformer with gpu-friendly sparsity and quantization.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Boost vision transformer with gpu-friendly sparsity and quantization

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.485611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 88d768d8-3359-4ad1-b447-c23fdc91e314 · outbound

This paper cites Width & depth pruning for vision transformers.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Width & depth pruning for vision transformers

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.464747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3f674856-9f03-4622-88b2-34a8b7de35e4 · outbound

This paper cites Unified Visual Transformer Compression.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Unified Visual Transformer Compression

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.190444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3653e378-8e32-4bd2-9629-a6609408d2bc · outbound

This paper cites AppAgent: Multimodal Agents as Smartphone Users.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices AppAgent: Multimodal Agents as Smartphone Users

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.195310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1128f696-60b7-4e44-a028-c2562ea3a7bf · outbound

This paper cites Tileflow: A framework for modeling fusion dataflow via tree-based analysis.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices Tileflow: A framework for modeling fusion dataflow via tree-based analysis

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.446934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4f5c5103-a284-4dfd-8fe5-48f6e81bd9de · outbound

This paper cites and Yang, K.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices and Yang, K

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T16:20:14.426817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 55b38dc0-c758-4f83-a6cd-dfa2cea1fcca · outbound

This paper cites write newline.

MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices write newline

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T16:20:03.210254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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