Paper Citation Record · LEDGER

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators

As of 23 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 2 inbound Pith citation observations for arXiv:2412.15548.

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

pith.paper-citation-record.v1
2412.15548 v1

Coverage vector

measured 71 of 71 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:23:49.339105Z

measured 73 of 73 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:50:51.744950Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T20:34:03.294898Z

Reference resolution

71 of 71 outbound references displayed

verified exact0
verified fuzzy71
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d0a6a575-1631-4281-a6e2-dee0f82cc752 · outbound

This paper cites Challenges/Opportunities to Enable Dependable Scale-out System with Groq Deterministic Tensor-Streaming Processors,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Challenges/Opportunities to Enable Dependable Scale-out System with Groq Deterministic Tensor-Streaming Processors,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.344863Z

Source-reported events for the cited work

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Observation 77e8aa7f-e2d6-457b-9278-03a85838e5ed · outbound

This paper cites BOOM- Explorer: RISC-V BOOM Microarchitecture Design Space Exploration Framework,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators BOOM- Explorer: RISC-V BOOM Microarchitecture Design Space Exploration Framework,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.333116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f6e71754-725a-4ce4-9270-7e5c5a1bd0a6 · outbound

This paper cites Transfer Learning for Bayesian Optimization: A Survey,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Transfer Learning for Bayesian Optimization: A Survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.321146Z

Source-reported events for the cited work

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Observation 52d4d7c3-4fd3-4520-b0c5-9fc3d37474f2 · outbound

This paper cites Autoencoders,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Autoencoders,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.309273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 118c82d3-5ddd-4682-a2a7-8f36d7ef6d91 · outbound

This paper cites Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.298885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bd70c049-7ec4-4517-94c5-1c246739b647 · outbound

This paper cites Eyeriss: An Energy- Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Eyeriss: An Energy- Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.287975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2da33bf2-b3c6-4861-b3c7-762170fcba60 · outbound

This paper cites Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.276350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b150b11-6930-47b4-beb0-133031080521 · outbound

This paper cites dMazeRun- ner: Executing Perfectly Nested Loops on Dataflow Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators dMazeRun- ner: Executing Perfectly Nested Loops on Dataflow Accelerators,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.264594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6e24c53e-6b5b-4b24-a768-42453487eb3d · outbound

This paper cites BERT: Pre- training of Deep Bidirectional Transformers for Language Understand- ing,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators BERT: Pre- training of Deep Bidirectional Transformers for Language Understand- ing,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.251866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 82106d72-f717-44a0-bb93-ba2109131ebc · outbound

This paper cites Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.240406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8cd8784d-0ac8-4d34-8ca6-1faaa77fcfad · outbound

This paper cites A Survey on Deep Learning and Its Applications,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Survey on Deep Learning and Its Applications,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.228158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 66d251fa-d25a-4bd0-9fe7-df75d6cbc31b · outbound

This paper cites Acceler- ating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Acceler- ating Scientific Applications With SambaNova Reconfigurable Dataflow Architecture,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.216606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c27227f0-b5c8-4c30-9a19-b360026d392f · outbound

This paper cites An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.203805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a4941f9d-1ddf-4b16-89dc-74692b3125c5 · outbound

This paper cites Physically Accurate Learning-Based Performance Prediction of Hardware-Accelerated ML Algorithms,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Physically Accurate Learning-Based Performance Prediction of Hardware-Accelerated ML Algorithms,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.191544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2a2e1f7b-db33-48b1-9471-4cc3f76bf7b5 · outbound

This paper cites Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.180004Z

Source-reported events for the cited work

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Observation cdfbebfe-7d93-4a2e-86bd-4b257314b985 · outbound

This paper cites Practical Transfer Learning for Bayesian Optimization,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Practical Transfer Learning for Bayesian Optimization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.169666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f5b6efff-6f50-4b98-bf5b-f69d74ae314a · outbound

This paper cites Tests for Rank Correlation Coefficients, I,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Tests for Rank Correlation Coefficients, I,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.153963Z

Source-reported events for the cited work

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Observation b6d843d0-d4e5-49a2-8466-47cc9580fa2d · outbound

This paper cites Multi-fidelity Optimiza- tion via Surrogate Modelling,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Multi-fidelity Optimiza- tion via Surrogate Modelling,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.138365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 52675dea-08dd-480a-b7c0-08246cd196d2 · outbound

This paper cites Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.123149Z

Source-reported events for the cited work

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Observation a444fcdf-acd0-4aff-b68a-a75b6ded941c · outbound

This paper cites Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.108074Z

Source-reported events for the cited work

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Observation a9634d39-e531-4ff4-aab1-6dfc75aaffb7 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Deep Residual Learning for Image Recognition,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.094782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 01cf43e8-5578-45cb-9af4-cd2e468506f1 · outbound

This paper cites DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.082865Z

Source-reported events for the cited work

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Observation d1b7b2ee-9779-4041-a510-e9ac59229714 · outbound

This paper cites Learning A Continuous and Reconstructible Latent Space for Hard- ware Accelerator Design,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Learning A Continuous and Reconstructible Latent Space for Hard- ware Accelerator Design,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.070717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1333563f-b3e9-444a-afe5-15836105ae56 · outbound

This paper cites Ten Lessons From Three Generations Shaped Google’s TPUv4i : Industrial Product,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Ten Lessons From Three Generations Shaped Google’s TPUv4i : Industrial Product,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.060421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5729a614-0926-4ffe-b6c7-f692b1b88c92 · outbound

This paper cites In-Datacenter Performance Analysis of a Tensor Processing Unit,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators In-Datacenter Performance Analysis of a Tensor Processing Unit,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.049811Z

Source-reported events for the cited work

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Observation 06ad2769-79f1-4999-9472-f36b53f53bac · outbound

This paper cites ConfuciuX: Autonomous Hard- ware Resource Assignment for DNN Accelerators using Reinforcement Learning,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators ConfuciuX: Autonomous Hard- ware Resource Assignment for DNN Accelerators using Reinforcement Learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.038053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 88f51a0c-d37d-45ec-b058-868dd860dde5 · outbound

This paper cites Firesim: FPGA- Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Firesim: FPGA- Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.025631Z

Source-reported events for the cited work

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Observation ba32030a-d073-4618-a6ee-4e36e1b4ebba · outbound

This paper cites A Learned Performance Model for Tensor Processing Units,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Learned Performance Model for Tensor Processing Units,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.013880Z

Source-reported events for the cited work

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Observation 3c4e7755-2e1a-4c9b-951b-e212100ce510 · outbound

This paper cites Full Stack Optimization of Transformer Inference: A Survey,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Full Stack Optimization of Transformer Inference: A Survey,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:50.001576Z

Source-reported events for the cited work

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Observation d3e90bd5-6b0e-4292-9baa-4af3f6c26ed4 · outbound

This paper cites Auto-Encoding Variational Bayes,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Auto-Encoding Variational Bayes,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.988670Z

Source-reported events for the cited work

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Observation aa60fca3-12a0-4d51-b47f-1129ba8a9e91 · outbound

This paper cites Spatial: A language and compiler for application accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Spatial: A language and compiler for application accelerators,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.974991Z

Source-reported events for the cited work

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Observation 28c04580-09d9-456c-be4c-9f1dc03d4ff6 · outbound

This paper cites ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.960932Z

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Observation 934d39f4-62cd-466f-a1ba-b7eaf98b730e · outbound

This paper cites On Information and Sufficiency,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators On Information and Sufficiency,

Reference 33

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Source-reported events for the cited work

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Observation bb18bd69-4b2e-4fc8-b55d-7ef3586abc91 · outbound

This paper cites Data-Driven Offline Optimization for Architecting Hardware Accelera- tors,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Data-Driven Offline Optimization for Architecting Hardware Accelera- tors,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.936735Z

Source-reported events for the cited work

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Observation 7f96a163-4db0-493e-851a-d27b5a4c90b0 · outbound

This paper cites MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.918468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0e5d57d0-f49a-4a75-ace4-3e2f59745cd5 · outbound

This paper cites MLIR: Scaling Compiler Infrastructure for Domain Specific Computation,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators MLIR: Scaling Compiler Infrastructure for Domain Specific Computation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.906161Z

Source-reported events for the cited work

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Observation 2be27045-2bcf-4937-90db-6d0ca4a888f0 · outbound

This paper cites Powering Extreme-Scale HPC with Cerebras Wafer- Scale Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Powering Extreme-Scale HPC with Cerebras Wafer- Scale Accelerators,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.895114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4eb25b9a-47ed-4101-ba09-0d7c49f240da · outbound

This paper cites A Study of Bayesian Neural Network Surrogates for Bayesian Optimization,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Study of Bayesian Neural Network Surrogates for Bayesian Optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.884264Z

Source-reported events for the cited work

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Observation e7a8b815-e5b1-4edf-835c-40bb521bf8fa · outbound

This paper cites Focal Loss for Dense Object Detection,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Focal Loss for Dense Object Detection,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.873845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2e9eff06-1f96-4a7a-ad3e-ae04ae3a577b · outbound

This paper cites NAAS: Neural Accelerator Architecture Search,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators NAAS: Neural Accelerator Architecture Search,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.863343Z

Source-reported events for the cited work

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Observation 2c05baf8-f612-4750-963d-342f512e7868 · outbound

This paper cites The AI Index 2023 Annual Report,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators The AI Index 2023 Annual Report,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.851960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fe7a7760-65b6-4fe7-81aa-027d67ef76a9 · outbound

This paper cites Mat ´ern, Spatial Variation, D.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Mat ´ern, Spatial Variation, D

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.840288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cf7c61fd-d731-407e-b48e-05a289056854 · outbound

This paper cites ZigZag: Enlarging Joint Architecture-Mapping Design Space Exploration for DNN Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators ZigZag: Enlarging Joint Architecture-Mapping Design Space Exploration for DNN Accelerators,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.830325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50b676d5-c5e8-4fd3-b4d4-915e02490953 · outbound

This paper cites STONNE: Enabling Cycle-Level Microarchitectural Simulation for DNN Inference Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators STONNE: Enabling Cycle-Level Microarchitectural Simulation for DNN Inference Accelerators,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.820657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3a5b4c94-2bb2-40ea-9b8b-25567db1ba08 · outbound

This paper cites Practical Design Space Exploration,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Practical Design Space Exploration,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.809348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 32aeecab-0235-469d-8d97-d96634834f65 · outbound

This paper cites Timeloop: A Systematic Approach to DNN Accelerator Evaluation,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Timeloop: A Systematic Approach to DNN Accelerator Evaluation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.797582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c5549d49-a14b-490e-aea1-68c8d9c9c797 · outbound

This paper cites Hardware/Software Co-design for Convolutional Neural Networks Acceleration: A Sur- vey and Open Issues,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Hardware/Software Co-design for Convolutional Neural Networks Acceleration: A Sur- vey and Open Issues,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.786314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9cbd4a92-fdbd-4136-8d2e-ddc6f4aba73c · outbound

This paper cites A Case for Efficient Accelerator Design Space Exploration via Bayesian Optimization,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Case for Efficient Accelerator Design Space Exploration via Bayesian Optimization,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.772995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d22c5a72-cba3-43c2-9aec-5efd3a38eba5 · outbound

This paper cites Stochastic Backprop- agation and Approximate Inference in Deep Generative Models,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Stochastic Backprop- agation and Approximate Inference in Deep Generative Models,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.759973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 476ff97b-4b5f-474c-a5f8-f00ec44e61f9 · outbound

This paper cites U-Net: Convolutional Net- works for Biomedical Image Segmentation,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators U-Net: Convolutional Net- works for Biomedical Image Segmentation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.749401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31003683-75b7-4f66-8d2f-cadb1b9ca35b · outbound

This paper cites Learning In- ternal Representations by Error Propagation,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Learning In- ternal Representations by Error Propagation,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.738246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b982e3b-0445-4a1b-9269-322a35c10986 · outbound

This paper cites Leveraging Domain Information for the Efficient Automated Design of Deep Learning Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Leveraging Domain Information for the Efficient Automated Design of Deep Learning Accelerators,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.727003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7755eec-5c11-4f52-80ac-d9254b9cb9ae · outbound

This paper cites AIrchitect: Automating Hardware Architecture and Mapping Optimization,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators AIrchitect: Automating Hardware Architecture and Mapping Optimization,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.717014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bf40b927-0bdb-4540-96fc-13ab3041ae46 · outbound

This paper cites A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.706543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ff2ba543-d854-41ab-a279-bf2cc597c8c6 · outbound

This paper cites Neural Architecture Search and Hardware Accelerator Co- Search: A Survey,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Neural Architecture Search and Hardware Accelerator Co- Search: A Survey,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.696466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fa2eb126-fdc7-42d5-8fc1-8e62c833ca11 · outbound

This paper cites An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.685781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a389a322-1cb7-4710-846e-b234e49b0468 · outbound

This paper cites Simba: Scaling Deep-Learning Inference with Multi-Chip-Module- Based Architecture,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Simba: Scaling Deep-Learning Inference with Multi-Chip-Module- Based Architecture,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.675064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6df38be7-defc-48a2-91a3-83e63084a2db · outbound

This paper cites Artificial Intelli- gence in the IoT Era: A Review of Edge AI Hardware and Software,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Artificial Intelli- gence in the IoT Era: A Review of Edge AI Hardware and Software,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.663230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8e6d903e-84a3-4d5e-9ab5-ebca7673eda7 · outbound

This paper cites On the Distribution of Points in a Cube and the Ap- proximate Evaluation of Integrals,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators On the Distribution of Points in a Cube and the Ap- proximate Evaluation of Integrals,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.537273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 16b175b1-539a-42a8-905a-1cca455bc0f8 · outbound

This paper cites Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental De- sign,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental De- sign,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.524086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6c985968-d924-4a57-911c-a0ee7dc7d736 · outbound

This paper cites Automated Design of Deep Neural Networks: A Survey and Unified Taxonomy,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Automated Design of Deep Neural Networks: A Survey and Unified Taxonomy,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.510529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 537ac339-4d1a-42b9-9bad-ddc7e2e17ac6 · outbound

This paper cites Compute Substrate for Software 2.0,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Compute Substrate for Software 2.0,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.496077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation df337a2d-c233-4e2c-937d-969092a248bf · outbound

This paper cites MAGNet: A Modular Accelerator Generator for Neural Networks,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators MAGNet: A Modular Accelerator Generator for Neural Networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.484017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 76b525da-150b-4b66-b16b-87e347e14afe · outbound

This paper cites Deep Kernel Learning,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Deep Kernel Learning,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.469820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 46b2fb97-aeed-48c9-994d-1bd4c6af3dc6 · outbound

This paper cites Few-Shot Bayesian Optimization with Deep Kernel Surrogates,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Few-Shot Bayesian Optimization with Deep Kernel Surrogates,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.456182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e1fd67c1-15ce-4ec2-8db2-4371dd9d0a41 · outbound

This paper cites SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.441893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ae5ffe1c-40a8-4346-9737-57d8bd06f8e3 · outbound

This paper cites HASCO: Towards Agile HArdware and Software CO-design for Tensor Compu- tation,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators HASCO: Towards Agile HArdware and Software CO-design for Tensor Compu- tation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.426577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6149ed02-a872-40f6-884d-2bed7507a2e5 · outbound

This paper cites Interstellar: Using Halide’s Scheduling Language to Analyze DNN Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Interstellar: Using Halide’s Scheduling Language to Analyze DNN Accelerators,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.411129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 492a7f4c-faaa-4f45-a7f8-efbbfd1e05d5 · outbound

This paper cites Apollo: Transferable Architecture Exploration,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators Apollo: Transferable Architecture Exploration,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.398364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d6a7f999-77cd-48c3-8733-bd5b242f5f54 · outbound

This paper cites A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.386955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6049b702-8e99-41a4-9cd6-da6d513e4430 · outbound

This paper cites A Comprehensive Survey on Transfer Learning,.

Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators A Comprehensive Survey on Transfer Learning,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:23:49.374940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 4ef72019-8aa5-4093-8af8-6ac74ce8f17a · inbound

DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration cites this paper.

DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T20:34:03.382134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a878697b-bc7c-4fc6-8a41-4e32e95efac9 · inbound

Fovea: Physical-Implication-Aware Wafer-Scale DSE with Decision-Domain-Guided Cross-Fidelity Refinement cites this paper.

Fovea: Physical-Implication-Aware Wafer-Scale DSE with Decision-Domain-Guided Cross-Fidelity Refinement Polaris: Multi-Fidelity Design Space Exploration of Deep Learning Accelerators

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T21:50:51.744950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.