Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator | Paper Citation Record

Observation feff1650-5512-4333-bc7f-d613e2abc38b · outbound

This paper cites Etalon: Holistic Performance Evaluation Framework for LLM Inference Systems.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Etalon: Holistic Performance Evaluation Framework for LLM Inference Systems

Reference 1

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Observation fb08ee07-d818-4ab1-b3c2-7509d7755767 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 2

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Observation f01a62a1-c212-41c3-b459-786b31024d0b · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Piqa: Reasoning about physical commonsense in natural language,

Reference 3

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Observation c7ba9cf5-9096-4800-9b53-67fa94a1226b · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 4

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Observation 28f049cc-0091-4420-bcbf-285874938aea · outbound

This paper cites 15.3 a 65nm 3t dynamic analog ram- based computing-in-memory macro and cnn accelerator with retention enhancement, adaptive analog sparsity and 44tops/w system energy efficiency,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator 15.3 a 65nm 3t dynamic analog ram- based computing-in-memory macro and cnn accelerator with retention enhancement, adaptive analog sparsity and 44tops/w system energy efficiency,

Reference 5

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Observation 8409119a-f975-465b-92e1-2547f0b4589c · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 6

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Observation be94c62b-0908-4d96-8646-dc9d7216ef70 · outbound

This paper cites Towards Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level Sparsity.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Towards Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level Sparsity

Reference 7

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Observation 7c3575eb-77e7-44bf-9365-960f9e3a1da8 · outbound

This paper cites Integrating nvidia deep learning accelerator (nvdla) with risc-v soc on firesim,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Integrating nvidia deep learning accelerator (nvdla) with risc-v soc on firesim,

Reference 8

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Observation 816f2d6a-c339-4941-aac0-aa102802f77c · outbound

This paper cites SparseGPT: Massive language models can be accurately pruned in one-shot,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator SparseGPT: Massive language models can be accurately pruned in one-shot,

Reference 9

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Observation b50e1cca-8265-4adf-8ac7-9c6fd2389271 · outbound

This paper cites A 5-nm 254-tops/w 221-tops/mm 2 fully-digital computing-in-memory macro supporting wide-range dynamic-voltage- frequency scaling and simultaneous mac and write operations,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator A 5-nm 254-tops/w 221-tops/mm 2 fully-digital computing-in-memory macro supporting wide-range dynamic-voltage- frequency scaling and simultaneous mac and write operations,

Reference 10

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Observation 3ae0549c-5f48-418f-a0ee-4e3340d4174c · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 11

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Observation e8a8ebf6-d390-4981-8c03-809a8c1ac92f · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:39.776256Z

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Observation f8dfdcf7-c4c0-4172-b270-c6554b6ac0a7 · outbound

This paper cites Sparsity-aware and re-configurable npu architecture for samsung flagship mobile soc,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Sparsity-aware and re-configurable npu architecture for samsung flagship mobile soc,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.445608Z

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Observation 22bda09e-5628-4bf7-b578-9f922554373a · outbound

This paper cites Vegeta: Vertically-integrated extensions for sparse/dense gemm tile acceleration on cpus,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Vegeta: Vertically-integrated extensions for sparse/dense gemm tile acceleration on cpus,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.429527Z

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Observation 40dc742b-5d3b-4b12-b8e4-8ba803f9102a · outbound

This paper cites Mixtral of Experts.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Mixtral of Experts

Reference 15

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Observation 29b20204-95bd-4824-bfdb-ebdf3ac01d1f · outbound

This paper cites Colonnade: A reconfigurable sram-based digital bit- serial compute-in-memory macro for processing neural networks,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Colonnade: A reconfigurable sram-based digital bit- serial compute-in-memory macro for processing neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.416050Z

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Observation a7f4da27-88c5-4cdb-8141-86dfb17bd647 · outbound

This paper cites Laxor: A bit-accurate bnn accelerator with latch-xor logic for local computing,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Laxor: A bit-accurate bnn accelerator with latch-xor logic for local computing,

Reference 17

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Observation c664d95e-6800-4dfa-be1a-f0650edc5944 · outbound

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

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Awq: Activation-aware weight quanti- zation for on-device llm compression and acceleration,

Reference 18

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Observation ef034e36-283e-4d01-8115-93165073cefc · outbound

This paper cites 33.2 a fully integrated analog reram based 78.4 tops/w compute-in-memory chip with fully parallel mac computing,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator 33.2 a fully integrated analog reram based 78.4 tops/w compute-in-memory chip with fully parallel mac computing,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.368998Z

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Observation fec4ce2b-e162-4afd-a23a-e209b548fb74 · outbound

This paper cites S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator S2ta: Exploiting structured sparsity for energy-efficient mobile cnn acceleration,

Reference 20

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Observation cebbceff-bbea-4cc7-86c4-0f8533207d63 · outbound

This paper cites An Analysis of Neural Language Modeling at Multiple Scales.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator An Analysis of Neural Language Modeling at Multiple Scales

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:39.830657Z

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Observation 7740e6a5-7649-41cc-85b7-70e94a6de7eb · outbound

This paper cites Introducing meta llama 3: The most capable openly available llm to date,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Introducing meta llama 3: The most capable openly available llm to date,

Reference 22

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Observation 741c9a68-bb6f-446f-894f-8d427b1382f5 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Accelerating Sparse Deep Neural Networks

Reference 23

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Observation ea9a6263-ec33-4302-81e1-78e97098e5bf · outbound

This paper cites Scnn: An accelerator for compressed-sparse convolutional neural networks,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Scnn: An accelerator for compressed-sparse convolutional neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.338531Z

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Observation 3d9673d9-00f4-47d6-8dc6-4566fa98def2 · outbound

This paper cites FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:39.849507Z

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Observation e2250370-b668-487f-bbe1-dd0c694b9875 · outbound

This paper cites MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization

Reference 26

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Observation 19b1bd7b-ebd7-4b34-b300-c6fb91a465ae · outbound

This paper cites Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference

Reference 27

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Observation 27eab695-f837-4151-9afc-9d683e024110 · outbound

This paper cites Deepscaletool: A tool for the accurate esti- mation of technology scaling in the deep-submicron era,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Deepscaletool: A tool for the accurate esti- mation of technology scaling in the deep-submicron era,

Reference 28

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Observation b805c6e2-3702-4750-a9e2-3bda45d77c94 · outbound

This paper cites Dnnweaver: From high-level deep network models to fpga acceleration,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Dnnweaver: From high-level deep network models to fpga acceleration,

Reference 29

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Observation 01979481-af26-4f28-a4b9-bc31527625f4 · outbound

This paper cites Towards vqa models that can read,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Towards vqa models that can read,

Reference 30

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Observation 922dc18b-f23d-47dc-9312-f5e1d34ba640 · outbound

This paper cites Sp-imc: A sparsity aware in-memory-computing macro in 28nm cmos with configurable sparse representation for highly sparse dnn workloads,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Sp-imc: A sparsity aware in-memory-computing macro in 28nm cmos with configurable sparse representation for highly sparse dnn workloads,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.298516Z

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Observation 2c59a99b-55cc-4004-81b3-792439f849a2 · outbound

This paper cites A fully-digital and row-pipelined compute-in-memory neural network accelerator with soc-level benchmarking for ar/vr applications,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator A fully-digital and row-pipelined compute-in-memory neural network accelerator with soc-level benchmarking for ar/vr applications,

Reference 32

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Observation 0f9baaa9-8a58-42b3-b106-8c9f5798265a · outbound

This paper cites A simple and effective pruning approach for large language models,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator A simple and effective pruning approach for large language models,

Reference 33

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Observation 90a266c4-d203-4ace-891a-bf43674befb2 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 34

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Observation 79b6dd9d-a4d4-471b-ba1c-a5fa3e2f6be5 · outbound

This paper cites Sdp: Co-designing algorithm, dataflow, and architecture for in-sram sparse nn acceleration,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Sdp: Co-designing algorithm, dataflow, and architecture for in-sram sparse nn acceleration,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.268520Z

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Observation 6e68ec14-afeb-4b99-89cf-52cb180c80c8 · outbound

This paper cites Highlight: Efficient and flexible dnn acceleration with hierarchical structured sparsity,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Highlight: Efficient and flexible dnn acceleration with hierarchical structured sparsity,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.254592Z

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Observation 1c7ab78a-537e-42c3-8244-0f2ba667a316 · outbound

This paper cites A task-centric angle of llm pre-trained weights through sparsity,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator A task-centric angle of llm pre-trained weights through sparsity,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.241471Z

Source-reported events for the cited work

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Observation 331331cc-b9bd-4f5b-ad0d-bbc9a4105c54 · outbound

This paper cites Outlier weighed layerwise sparsity: A missing secret sauce for pruning llms to high sparsity,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Outlier weighed layerwise sparsity: A missing secret sauce for pruning llms to high sparsity,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.227953Z

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Observation a168e307-4eb4-429e-a4a5-50c6103df820 · outbound

This paper cites Compute-in-memory chips for deep learning: Recent trends and prospects,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Compute-in-memory chips for deep learning: Recent trends and prospects,

Reference 39

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Observation 080ca778-31e3-4487-8262-2b95ec81283d · outbound

This paper cites 15.2 a 2.75-to-75.9 tops/w computing-in-memory nn processor supporting set-associate block-wise zero skipping and ping- pong cim with simultaneous computation and weight updating,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator 15.2 a 2.75-to-75.9 tops/w computing-in-memory nn processor supporting set-associate block-wise zero skipping and ping- pong cim with simultaneous computation and weight updating,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.208706Z

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Observation 9b9bce9e-a36b-402e-98a8-026e8992f655 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:39.925019Z

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Observation d96b563b-51a0-4dbc-a7bc-9693216e49b0 · outbound

This paper cites A 28-nm 18.7 tops/mm2 89.4-to-234.6 tops/w 8b single-finger edram compute- in-memory macro with bit-wise sparsity aware and kernel-wise weight update/refresh,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator A 28-nm 18.7 tops/mm2 89.4-to-234.6 tops/w 8b single-finger edram compute- in-memory macro with bit-wise sparsity aware and kernel-wise weight update/refresh,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.196376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation feb2bccb-3a17-4750-8c1c-da429d3217f3 · outbound

This paper cites Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T11:55:39.934771Z

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Observation 3139199b-6603-4da6-be46-fcbe77bdd443 · outbound

This paper cites A digital sram computing-in-memory design utilizing activation unstructured sparsity for high-efficient dnn inference,.

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator A digital sram computing-in-memory design utilizing activation unstructured sparsity for high-efficient dnn inference,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:55:40.183762Z

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