Paper Citation Record · LEDGER

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design

As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2506.03474.

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

pith.paper-citation-record.v1
2506.03474 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:07:21.356004Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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 exact0
verified fuzzy20
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 35865db8-31cd-46d2-a7d1-37f7ae683c32 · outbound

This paper cites Genetic algorithms.Scientific american, 267(1):66–73, 1992.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Genetic algorithms.Scientific american, 267(1):66–73, 1992

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:20.997755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb5d07f7-6d75-40bc-87eb-4abbcba92073 · outbound

This paper cites Bayesian optimization with adaptive surrogate models for automated experimental design.Npj Computational Materials, 7(1):194, 2021.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Bayesian optimization with adaptive surrogate models for automated experimental design.Npj Computational Materials, 7(1):194, 2021

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:23.526083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 134105c2-35dc-4970-8fae-a5a25ce7de95 · outbound

This paper cites Reinforcement learning: A survey.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Reinforcement learning: A survey

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:21.080165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0489a758-aed6-414b-963b-c70f41a39dfb · outbound

This paper cites Proximal Policy Optimization Algorithms.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Proximal Policy Optimization Algorithms

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:21.116437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dda2cd4-9d9e-4395-9990-d49b3a62b821 · outbound

This paper cites Archgym: An open-source gymnasium for machine learning assisted architecture design.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Archgym: An open-source gymnasium for machine learning assisted architecture design

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:23.324461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 084e4fea-eb74-4480-b6a0-1a607356b659 · outbound

This paper cites Delving into macro placement with reinforcement learning.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Delving into macro placement with reinforcement learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:23.206672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb36bb48-02ec-4e09-9e5c-0c3995410d24 · outbound

This paper cites Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows.Physical Review Fluids, 6(5):053902, 2021.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows.Physical Review Fluids, 6(5):053902, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:23.112071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c5413f7-ee90-496d-a6f6-fd8fd64a4628 · outbound

This paper cites Mortazavi, Tiancheng Qin, and Ning Yan.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Mortazavi, Tiancheng Qin, and Ning Yan

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:23.067987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b76630e0-5f4b-45d6-8204-6f679a6a77ca · outbound

This paper cites Mortazavi, and Ning Yan.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Mortazavi, and Ning Yan

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.923692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7dce49e3-f7f0-4132-a342-572572c84e54 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:21.324193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edc6cd44-67b9-467c-a56b-0214db37c25b · outbound

This paper cites Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks.IEEE journal of solid-state circuits, 52(1):127–138, 2016.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks.IEEE journal of solid-state circuits, 52(1):127–138, 2016

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.853170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2dae4476-1d33-4d54-9327-042605777dab · outbound

This paper cites Nvdla deep learning accelerator.http://nvdla.org, 2017.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Nvdla deep learning accelerator.http://nvdla.org, 2017

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.717993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9229a6c9-ec5c-4dfc-8aa1-82bf64760183 · outbound

This paper cites Shidiannao: Shifting vision processing closer to the sensor.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Shidiannao: Shifting vision processing closer to the sensor

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.673485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 067c533e-7983-41b9-b85b-b60241ee804f · outbound

This paper cites Simulated annealing.Statistical science, 8(1):10–15, 1993.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Simulated annealing.Statistical science, 8(1):10–15, 1993

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:21.332737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a826be7-de3d-439d-ae65-d6c71ca09344 · outbound

This paper cites Towards automated risc-v mi- croarchitecture design with reinforcement learning.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Towards automated risc-v mi- croarchitecture design with reinforcement learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.574734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e3a89f21-6c12-47a2-9609-ad542fb1c13b · outbound

This paper cites Confuciux: Autonomous hardware resource as- signment for dnn accelerators using reinforcement learning.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Confuciux: Autonomous hardware resource as- signment for dnn accelerators using reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.490965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f31a6499-0989-49ca-92e0-8c70fe2346b2 · outbound

This paper cites Gamma: Automating the hw mapping of dnn models on accelerators via genetic algorithm.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Gamma: Automating the hw mapping of dnn models on accelerators via genetic algorithm

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.383082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a73a7892-c052-4a72-a94c-76df9ec0a294 · outbound

This paper cites Flextensor: An automatic schedule exploration and optimization framework for tensor computation on heterogeneous system.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Flextensor: An automatic schedule exploration and optimization framework for tensor computation on heterogeneous system

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.299895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f139379-be35-4585-b51e-03a032f1d9fd · outbound

This paper cites Digamma: Domain-aware genetic algorithm for hw-mapping co-optimization for dnn accelerators.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Digamma: Domain-aware genetic algorithm for hw-mapping co-optimization for dnn accelerators

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.152090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2cf1039b-5c26-4bfd-a00e-4b024c46af8d · outbound

This paper cites Hasco: To- wards agile hardware and software co-design for tensor computation.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Hasco: To- wards agile hardware and software co-design for tensor computation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:22.081237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d92c6904-bb62-4653-bf8f-227f46309b0c · outbound

This paper cites Unico: Unified hardware software co-optimization for robust neural network acceleration.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Unico: Unified hardware software co-optimization for robust neural network acceleration

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:21.983834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d9aae89-4bbe-4656-a7b3-085f02f4867b · outbound

This paper cites A mechanistic performance model for superscalar out-of-order processors.ACM Transactions on Computer Systems (TOCS), 27(2):1–37, 2009.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design A mechanistic performance model for superscalar out-of-order processors.ACM Transactions on Computer Systems (TOCS), 27(2):1–37, 2009

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:21.835580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7447214d-7e81-4771-b36b-9bdd8b70b765 · outbound

This paper cites Revisiting Design Choices in Proximal Policy Optimization.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Revisiting Design Choices in Proximal Policy Optimization

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:07:21.350107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fced237-06a8-4545-a8b5-dc299140a08b · outbound

This paper cites Simba: Scaling deep-learning inference with multi-chip-module-based architecture.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Simba: Scaling deep-learning inference with multi-chip-module-based architecture

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:21.676150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f66781bf-2b3e-4fda-8d1b-575fd1cf4c22 · outbound

This paper cites Maestro: A data-centric approach to understand reuse, performance, and hardware cost of dnn mappings.IEEE micro, 40(3):20–29, 2020.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Maestro: A data-centric approach to understand reuse, performance, and hardware cost of dnn mappings.IEEE micro, 40(3):20–29, 2020

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:21.536190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 097705d5-7e43-43f5-89ea-8cdc4b04901d · outbound

This paper cites Timeloop: A systematic approach to dnn accelerator evaluation.

CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design Timeloop: A systematic approach to dnn accelerator evaluation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:07:21.431704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.