Paper Citation Record · LEDGER

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening

As of 20 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2608.10506.

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

pith.paper-citation-record.v1
2608.10506 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:23:07.316138Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

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

26 of 26 outbound references displayed

verified exact1
verified fuzzy15
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 64f7202e-e778-4e59-995b-0532999e44a2 · outbound

This paper cites Maple: Microprocessor a priori for latency estimation.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Maple: Microprocessor a priori for latency estimation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.598067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 33a07804-88ea-4b11-badd-3601c00e4ae8 · outbound

This paper cites Neuralpower: Predict and deploy energy-efficient convolu- tional neural networks.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Neuralpower: Predict and deploy energy-efficient convolu- tional neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.587026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c24aea5-4791-42d7-9010-d7ce96d5f42f · outbound

This paper cites ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.231045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afdf49fc-5058-4f44-9c7d-abbd2f61b8de · outbound

This paper cites An Analysis of Deep Neural Network Models for Practical Applications.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening An Analysis of Deep Neural Network Models for Practical Applications

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.235299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 076cb8ed-0606-4ea1-beab-8b4b47697857 · outbound

This paper cites Energy-based tuning of convolu- tional neural networks on multi-gpus.Concurrency and Computation: Practice and Experience, 31(21):e4786, 2019.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Energy-based tuning of convolu- tional neural networks on multi-gpus.Concurrency and Computation: Practice and Experience, 31(21):e4786, 2019

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.576174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 00db2303-eef3-44cd-ac01-710ca1e31e15 · outbound

This paper cites Xgboost: A scalable tree boosting system.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Xgboost: A scalable tree boosting system

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.243594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a027f71-8b6f-4e7f-952d-39525ef71591 · outbound

This paper cites an unresolved cited work.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.247549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00cc8bc4-a9fb-49d9-812e-47cd02ad01a5 · outbound

This paper cites Extremely randomized trees.Machine Learning, 63(1):3–42, Mar 2006.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Extremely randomized trees.Machine Learning, 63(1):3–42, Mar 2006

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.551865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 86ba8e02-93f1-4ada-91c2-c12b3d9164fe · outbound

This paper cites Dvfs- aware dnn inference on gpus: Latency modeling and performance analysis.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Dvfs- aware dnn inference on gpus: Latency modeling and performance analysis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.540535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 988a338e-df55-4a50-afd0-748e48175ae3 · outbound

This paper cites Deep residual learning for image recognition.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Deep residual learning for image recognition

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.258851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcb27f3d-09f1-43a3-9a7c-c5cdaa124025 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Lightgbm: A highly efficient gradient boosting decision tree

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.262507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fce2b9a4-59e6-40d8-92bd-133382939f38 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.266146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6733598b-2381-42bf-a785-aa526d3eed91 · outbound

This paper cites Forecasting gpu performance for deep learning training and inference.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Forecasting gpu performance for deep learning training and inference

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.510501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 46a315fa-3003-482b-b5ce-8443d05fb21a · outbound

This paper cites fvcore: Facebook’s core library for computer vision research, 2020.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening fvcore: Facebook’s core library for computer vision research, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.499344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41d07fa1-5132-49f9-8777-1f61c8dc4435 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.Advances in neural information processing systems, 32, 2019.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Pytorch: An imperative style, high- performance deep learning library.Advances in neural information processing systems, 32, 2019

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.276594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd1d2355-1d8e-4831-b2d5-b73a7f5dfd37 · outbound

This paper cites Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:23:07.352515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 02c62f4b-b812-4757-b30f-70b548e17060 · outbound

This paper cites Fine- grained energy profiling for deep convolutional neural networks on the jetson tx1.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Fine- grained energy profiling for deep convolutional neural networks on the jetson tx1

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.480637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57fca482-711e-48cd-99e4-3f06de413677 · outbound

This paper cites Memory requirements for convolutional neural network hardware accelerators.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Memory requirements for convolutional neural network hardware accelerators

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.469560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f121320c-d1e9-4d1b-8a17-0ef6daeeba69 · outbound

This paper cites Multi-target regression via input space expansion: treating targets as inputs.Machine Learning, 104(1):55–98, Feb 2016.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Multi-target regression via input space expansion: treating targets as inputs.Machine Learning, 104(1):55–98, Feb 2016

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.458576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 080350da-7f6e-4453-9bbc-a5a80a853bb5 · outbound

This paper cites Hyperpower: Power-and memory-constrained hyper- parameter optimization for neural networks.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Hyperpower: Power-and memory-constrained hyper- parameter optimization for neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.446686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ace4d919-74ce-4800-b0df-e56028ff8b6b · outbound

This paper cites Energy and policy considerations for deep learning in nlp.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Energy and policy considerations for deep learning in nlp

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.298211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60f08f81-5083-4ccb-9f9e-9004b447955a · outbound

This paper cites Characterizing deep learning training workloads on alibaba-pai.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Characterizing deep learning training workloads on alibaba-pai

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.428797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d8cfca72-d0df-4813-9f25-1a09818d1db0 · outbound

This paper cites Roofline: an insightful visual performance model for multicore architectures.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Roofline: an insightful visual performance model for multicore architectures

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T14:23:07.305414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b752f7e-d408-474a-8ea6-7ef168d9a6fb · outbound

This paper cites Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.411071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec8457e1-b2ac-4536-b924-4d8bba0c71db · outbound

This paper cites Designing energy- efficient convolutional neural networks using energy-aware pruning.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Designing energy- efficient convolutional neural networks using energy-aware pruning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.399015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 466ccb7b-993b-40ee-80f3-3e758a9cd0fa · outbound

This paper cites Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices.

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:23:07.387016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.