Paper Citation Record · LEDGER

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.23437.

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

pith.paper-citation-record.v1
2507.23437 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

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measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 78f7b5df-d574-45a6-9e58-80fc2d5c802f · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.981708Z

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Observation bc123978-c11f-429b-b755-fa198fc568b0 · outbound

This paper cites Neural Architecture Search: Insights from 1000 Papers.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Neural Architecture Search: Insights from 1000 Papers

Reference 2

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Observation 4985d5b8-8872-4fb0-b67c-27f0ae34b905 · outbound

This paper cites Multiobjective tree-structured parzen estimator,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Multiobjective tree-structured parzen estimator,

Reference 3

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Observation 95922183-2291-4a7a-98f6-1969c12a3b67 · outbound

This paper cites Edgeyolo: An edge-real- time object detector,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Edgeyolo: An edge-real- time object detector,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.955200Z

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Observation e2b88a13-1fb9-4961-9a31-b9d83ce9abaf · outbound

This paper cites Edge computing for real-time internet of things applications: Future internet revolution,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Edge computing for real-time internet of things applications: Future internet revolution,

Reference 5

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Observation 77a51f38-341d-499b-a8e9-810892a97774 · outbound

This paper cites Making accurate object detection at the edge: Review and new approach,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Making accurate object detection at the edge: Review and new approach,

Reference 6

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Observation a133ab54-e034-48ca-950b-793dff18b477 · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 7

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Source-reported events for the cited work

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Observation 23756e30-736e-42f7-bdfb-e3080b9ac17a · outbound

This paper cites Pabo: Pseudo agent-based multi-objective bayesian hyperparameter optimization for efficient neural accelerator design,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Pabo: Pseudo agent-based multi-objective bayesian hyperparameter optimization for efficient neural accelerator design,

Reference 8

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Observation 8c416242-d41e-4212-a949-bbc629941f39 · outbound

This paper cites Bayesian multi-objective hyperparameter optimization for accurate, fast, and efficient neural network accelerator design,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Bayesian multi-objective hyperparameter optimization for accurate, fast, and efficient neural network accelerator design,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.903685Z

Source-reported events for the cited work

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Observation 2ee1dc99-22ea-4c9a-86c7-43ebd49bcdca · outbound

This paper cites Neural architecture search for in-memory computing-based deep learning accelerators,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Neural architecture search for in-memory computing-based deep learning accelerators,

Reference 10

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Source-reported events for the cited work

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Observation d7fe0b6a-ca6d-4bac-8959-090d37d8633c · outbound

This paper cites Pymoo: Multi-objective optimization in python,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Pymoo: Multi-objective optimization in python,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.877678Z

Source-reported events for the cited work

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Observation 3d07b3a3-e9c8-4b91-a006-93903da8606e · outbound

This paper cites A fast and elitist multiobjective genetic algorithm: Nsga-ii,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design A fast and elitist multiobjective genetic algorithm: Nsga-ii,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.865468Z

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Observation b903ed7a-3b16-49a8-9f19-d2ac29dab059 · outbound

This paper cites Sparse gaussian processes using pseudo-inputs,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Sparse gaussian processes using pseudo-inputs,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.852381Z

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Observation 7a1410dc-729c-42de-997e-b965b707032c · outbound

This paper cites Defines: En- abling fast exploration of the depth-first scheduling space for dnn accel- erators through analytical modeling,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Defines: En- abling fast exploration of the depth-first scheduling space for dnn accel- erators through analytical modeling,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.839363Z

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Observation ee741680-b1a1-4e7c-9046-828a438cdb37 · outbound

This paper cites Rbflex-nas: Training-free neural architecture search using radial basis function kernel and hyperparameter detection,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Rbflex-nas: Training-free neural architecture search using radial basis function kernel and hyperparameter detection,

Reference 15

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Source-reported events for the cited work

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Observation 60aad7e2-5df6-47c0-bc2f-ba7841b79027 · outbound

This paper cites Nats-bench: Benchmarking nas algorithms for architecture topology and size,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Nats-bench: Benchmarking nas algorithms for architecture topology and size,

Reference 16

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Observation 939de935-2e3f-4cf6-9348-ae9eeb59646a · outbound

This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 17

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Observation ba353dd5-cc9e-4e83-a13a-53602badc62f · outbound

This paper cites Introduction to gaussian processes,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Introduction to gaussian processes,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.799848Z

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Observation edf65dbe-dfbf-49b3-ae8b-58545e79990b · outbound

This paper cites Improving the expected improvement algorithm,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Improving the expected improvement algorithm,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.786584Z

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Observation bdecdf3e-b1df-4a40-baaa-8774ee76f4a6 · outbound

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Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search,

Reference 20

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Observation 7cf94538-bbd1-4d25-9feb-3a5d147f5b50 · outbound

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Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Nas-bench-nlp: neural architecture search benchmark for natural language processing,

Reference 21

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Observation 91bc1efa-d04b-4999-b76f-a1813f960593 · outbound

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Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Gaussian processes for machine learning,

Reference 22

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Observation 3b09c785-63eb-4581-88f5-ec4913efaaf0 · outbound

This paper cites Sur la r ´esolution num ´erique des syst `emes d’ ´equations lin´eaires,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Sur la r ´esolution num ´erique des syst `emes d’ ´equations lin´eaires,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.733759Z

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Observation 6e18490c-7dde-4e51-be16-c58ea265ad99 · outbound

This paper cites Neural architec- ture search without training,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Neural architec- ture search without training,

Reference 24

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Observation 7f3acc6a-c80f-4cf3-bdc6-4c5bec77f2a1 · outbound

This paper cites Network pruning via transformable architecture search,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Network pruning via transformable architecture search,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.708619Z

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Observation 2e29d33f-9da8-429c-8e2b-a6bb1c2c6f52 · outbound

This paper cites Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.695388Z

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Observation 84de8bae-6b17-4fd6-bd74-bf9920df2780 · outbound

This paper cites Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

Reference 27

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Observation 9ae4e58f-f9ac-42a4-9c8e-5b260cfa5d73 · outbound

This paper cites ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients

Reference 28

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Observation e08de04f-f39a-49f2-a9d7-b3b7955fe83a · outbound

This paper cites System-level design and integration of a prototype ar/vr hardware featuring a custom low-power dnn accelerator chip in 7nm technology for codec avatars,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design System-level design and integration of a prototype ar/vr hardware featuring a custom low-power dnn accelerator chip in 7nm technology for codec avatars,

Reference 29

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Observation 57452830-d475-4619-bd20-eec19c5cc13d · outbound

This paper cites Elsa: A throughput-optimized design of an lstm accelerator for energy-constrained devices,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Elsa: A throughput-optimized design of an lstm accelerator for energy-constrained devices,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.669929Z

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Observation fcf06051-dd3d-4417-8a2f-4a264519c195 · outbound

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Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design E-rnn: Design optimization for efficient recurrent neural networks in fpgas,

Reference 31

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Observation 71e74769-f5e9-4fa8-a12d-ad8df771662a · outbound

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Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design E-pur: An energy- efficient processing unit for recurrent neural networks,

Reference 32

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Observation c4724e9f-17a1-4460-a274-347956249fb2 · outbound

This paper cites Vau da muntanialas: Energy-efficient multi-die scalable acceleration of rnn inference,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Vau da muntanialas: Energy-efficient multi-die scalable acceleration of rnn inference,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.630921Z

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Observation b9613555-ba5d-44f3-87fb-d8df670958ab · outbound

This paper cites MOBO: A new software for multi-objective building performance optimization,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design MOBO: A new software for multi-objective building performance optimization,

Reference 34

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Observation 241f1182-7a26-460d-8c52-35db1e4d1b54 · outbound

This paper cites Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.604032Z

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Observation 1a2ac9dc-e370-469d-a26a-52a352f49d3b · outbound

This paper cites Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.590873Z

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Observation 026b7505-0dc2-4b24-a71b-79fc7a4caca9 · outbound

This paper cites Parego: A hybrid algorithm with on-line landscape ap- proximation for expensive multiobjective optimization problems,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Parego: A hybrid algorithm with on-line landscape ap- proximation for expensive multiobjective optimization problems,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.576073Z

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Observation 96d66c44-8beb-44f3-939c-0c9e2a72bdae · outbound

This paper cites Distributed computing of pareto- optimal solutions with evolutionary algorithms,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Distributed computing of pareto- optimal solutions with evolutionary algorithms,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.561699Z

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 20db0480-33f0-4316-ad03-c86c7042c3a2 · outbound

This paper cites From a pareto front to pareto regions: A novel standpoint for multiobjective optimization,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design From a pareto front to pareto regions: A novel standpoint for multiobjective optimization,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.546662Z

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 16f98d4f-4424-48f5-9b75-a4dbe1e05855 · outbound

This paper cites Multiobjective optimization using evolutionary algorithms—a comparative case study,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Multiobjective optimization using evolutionary algorithms—a comparative case study,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.533299Z

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 981326ac-aad7-4748-9e0c-0e4c625a04cc · outbound

This paper cites Sms-emoa: Multiobjective selection based on dominated hypervolume,.

Coflex: Enhancing HW-NAS with Sparse Gaussian Processes for Efficient and Scalable DNN Accelerator Design Sms-emoa: Multiobjective selection based on dominated hypervolume,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:50:47.520157Z

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.

Pith citing papers

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