Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning | Paper Citation Record

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This paper cites Backpropagation and stochastic gradi- ent descent method.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Backpropagation and stochastic gradi- ent descent method

Reference 1

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This paper cites AMD Alveo U55C Data Center Accelera- tor Card.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning AMD Alveo U55C Data Center Accelera- tor Card

Reference 2

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Observation 68aebf0b-b4da-4462-946d-561df6cb2b00 · outbound

This paper cites VitisNetP4 IP for Adaptive SoCs and FPGAs.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning VitisNetP4 IP for Adaptive SoCs and FPGAs

Reference 3

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This paper cites AMBA AXI4-Stream Protocol Specification.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning AMBA AXI4-Stream Protocol Specification

Reference 4

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Observation 0af751fc-a588-4156-ab3b-f831bb13b59a · outbound

This paper cites Toward formally verifying congestion control behavior.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Toward formally verifying congestion control behavior

Reference 5

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Observation e8a93cbb-63df-447f-a77d-6a8bbe153382 · outbound

This paper cites RPM: Reverse Path Congestion Marking on P4 Programmable Switches.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning RPM: Reverse Path Congestion Marking on P4 Programmable Switches

Reference 6

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Observation 59c0fd3a-1268-45c2-a6b9-e4cb3285163d · outbound

This paper cites P4: Programming protocol-independent packet processors.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning P4: Programming protocol-independent packet processors

Reference 7

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This paper cites Trading latency for compute in the net- work.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Trading latency for compute in the net- work

Reference 8

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Observation e88000b5-45a4-4784-b9f5-922fd34bfd7d · outbound

This paper cites Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training

Reference 9

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This paper cites Boosting distributed machine learning training through loss-tolerant transmission protocol.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Boosting distributed machine learning training through loss-tolerant transmission protocol

Reference 10

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Observation a2f12cfe-7e8f-4fdc-9a3a-cb85b8455955 · outbound

This paper cites Scaling multi- agent reinforcement learning with selective parameter sharing.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Scaling multi- agent reinforcement learning with selective parameter sharing

Reference 11

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Observation 7c57974d-8a4d-4188-b015-03342d13f94b · outbound

This paper cites Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning

Reference 12

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This paper cites Cisco N9300 Series Smart Switches.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Cisco N9300 Series Smart Switches

Reference 13

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Observation 770421a0-4ba5-414d-82db-c1d38e9259c3 · outbound

This paper cites Accelerating neural network training with distributed asynchronous and selective optimiza- tion (DASO).

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Accelerating neural network training with distributed asynchronous and selective optimiza- tion (DASO)

Reference 14

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Observation 631e721a-c0c3-4b41-80f5-8587641dde3c · outbound

This paper cites Toward Understanding the Impact of Staleness in Distributed Machine Learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Toward Understanding the Impact of Staleness in Distributed Machine Learning

Reference 15

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Observation 6d647ee5-d119-47eb-b4c8-e254878fa2fe · outbound

This paper cites Distributed Deep Learning Using Synchronous Stochastic Gradient Descent.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Distributed Deep Learning Using Synchronous Stochastic Gradient Descent

Reference 16

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Observation f0b2c2c8-45a9-4857-a692-69b0676152b5 · outbound

This paper cites Z3: An ef- ficient smt solver.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Z3: An ef- ficient smt solver

Reference 17

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Observation 4f06f227-aa8d-4a8a-8684-051fbdab491a · outbound

This paper cites Large scale distributed deep networks.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Large scale distributed deep networks

Reference 18

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Observation 56f36b59-13b4-4de7-8b99-58e785d2d603 · outbound

This paper cites Distributed proxi- mal policy optimization for contention-based spectrum access.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Distributed proxi- mal policy optimization for contention-based spectrum access

Reference 19

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This paper cites P4-enabled network-assisted congestion feedback: A case for nacks.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning P4-enabled network-assisted congestion feedback: A case for nacks

Reference 20

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Observation f356bc8b-b9fc-44fe-8288-87dd8cd0b4f5 · outbound

This paper cites Scalable hierarchical aggregation protocol (SHArP): A hardware architecture for efficient data reduction.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Scalable hierarchical aggregation protocol (SHArP): A hardware architecture for efficient data reduction

Reference 21

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This paper cites Intel ® Tofino Intelligent Fabric Processors.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Intel ® Tofino Intelligent Fabric Processors

Reference 22

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Observation d8ce5977-3329-4e5b-9d7c-161d3519c517 · outbound

This paper cites Accelerating deep learning us- ing multiple GPUs and FPGA-based 10GbE switch.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Accelerating deep learning us- ing multiple GPUs and FPGA-based 10GbE switch

Reference 23

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Observation 54b3814b-8d36-4b21-bd05-1a3eac6a5ef4 · outbound

This paper cites The art of computer systems performance analysis.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning The art of computer systems performance analysis

Reference 24

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Observation 75a411a7-ddff-4caf-a80b-823db7056df9 · outbound

This paper cites Reinforcement learning: A survey.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Reinforcement learning: A survey

Reference 25

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Observation 092bcbad-563c-475a-9985-cf69fc1d3778 · outbound

This paper cites Parameter sharing reinforcement learning architecture for multi agent driving.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Parameter sharing reinforcement learning architecture for multi agent driving

Reference 26

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Observation 39dc4a29-9c7d-4d87-865b-5d2297445ba9 · outbound

This paper cites Rein- forcement learning in robotics: A survey.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Rein- forcement learning in robotics: A survey

Reference 27

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Observation 23ab3fc3-dde8-4795-8493-80d329b27f9d · outbound

This paper cites ATP: In-network aggregation for multi-tenant learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning ATP: In-network aggregation for multi-tenant learning

Reference 28

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This paper cites Andersen, Jun Woo Park, Alexander J.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Andersen, Jun Woo Park, Alexander J

Reference 29

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This paper cites Communication efficient distributed machine learn- ing with the parameter server.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Communication efficient distributed machine learn- ing with the parameter server

Reference 30

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This paper cites Parameter server for distributed machine learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Parameter server for distributed machine learning

Reference 31

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This paper cites Accelerating dis- tributed reinforcement learning with in-switch comput- ing.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Accelerating dis- tributed reinforcement learning with in-switch comput- ing

Reference 32

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This paper cites Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training

Reference 33

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This paper cites Deep Reinforcement Learning: An Overview.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Deep Reinforcement Learning: An Overview

Reference 34

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This paper cites RLlib: Abstractions for distributed reinforcement learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning RLlib: Abstractions for distributed reinforcement learning

Reference 35

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This paper cites Asynchronous Local-SGD Training for Language Modeling.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Asynchronous Local-SGD Training for Language Modeling

Reference 36

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Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning High-throughput synchronous deep RL

Reference 37

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Observation 6f943ced-c72b-44d8-8c67-abdf02fac0dd · outbound

This paper cites FPGA-based AI smart NICs for scalable distributed AI training systems.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning FPGA-based AI smart NICs for scalable distributed AI training systems

Reference 38

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This paper cites A Survey of DeepSeek Models.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning A Survey of DeepSeek Models

Reference 39

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Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Federated learning with buffered asynchronous aggregation

Reference 40

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This paper cites ns-3 Network Simulator.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning ns-3 Network Simulator

Reference 41

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This paper cites Di- rect preference optimization: Your language model is secretly a reward model.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Di- rect preference optimization: Your language model is secretly a reward model

Reference 42

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This paper cites Smaller world models for reinforcement learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Smaller world models for reinforcement learning

Reference 43

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This paper cites An overview of gradient descent optimization algorithms.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning An overview of gradient descent optimization algorithms

Reference 44

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This paper cites On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning On the Convergence Analysis of Asynchronous SGD for Solving Consistent Linear Systems

Reference 45

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 46

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This paper cites Scaling distributed machine learning with In-Network aggregation.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Scaling distributed machine learning with In-Network aggregation

Reference 47

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This paper cites Proximal Policy Optimization Algorithms.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 48

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This paper cites Addressing Stale Gradients in Scalable Federated Deep Reinforcement Learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Addressing Stale Gradients in Scalable Federated Deep Reinforcement Learning

Reference 49

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This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 50

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This paper cites A survey on distributed machine learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning A survey on distributed machine learning

Reference 51

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Observation c4a367d0-4380-43e1-a007-358e861a21c6 · outbound

This paper cites Rat-resilient allreduce tree for distributed machine learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Rat-resilient allreduce tree for distributed machine learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.688034Z

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Observation 157117be-d00d-4500-ae35-c019b9b13501 · outbound

This paper cites Domain-specific Communication Optimization for Distributed DNN Training.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Domain-specific Communication Optimization for Distributed DNN Training

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:21:39.216326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ce67d38e-5501-40c6-bebf-575c694cf407 · outbound

This paper cites Pufferfish: Communication-efficient Models At No Extra Cost.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Pufferfish: Communication-efficient Models At No Extra Cost

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.614739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9ef58144-5fc7-4336-b166-450631f24396 · outbound

This paper cites DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:38.646136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32dbdaef-a712-47f5-aaf5-ced212fa09d5 · outbound

This paper cites Reinforcement learning in autonomous driving.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Reinforcement learning in autonomous driving

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.571539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1201208d-0b68-466b-a379-351d3094c4a7 · outbound

This paper cites Asynchronous actor-critic for multi-agent reinforcement learning.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Asynchronous actor-critic for multi-agent reinforcement learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.548126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a827eddc-42fb-4aa0-8ce9-0668883cbda0 · outbound

This paper cites Open-NIC Project.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Open-NIC Project

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.522523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 52cca63b-3547-4266-b52e-8ffa04355468 · outbound

This paper cites Using Trio: Juniper Networks’ programmable chipset-for emerging in-network applications.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Using Trio: Juniper Networks’ programmable chipset-for emerging in-network applications

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.501223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 79a418d5-78eb-41e7-b274-8b26be506b90 · outbound

This paper cites Age of information: An introduction and survey.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Age of information: An introduction and survey

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.477532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea8c24dc-590d-443e-9772-92d1bf6d1fb2 · outbound

This paper cites Stellaris: Staleness-Aware Distributed 15 Reinforcement Learning with Serverless Computing.

Shesha: Opportunistic In-network Acceleration of Asynchronous Distributed Reinforcement Learning Stellaris: Staleness-Aware Distributed 15 Reinforcement Learning with Serverless Computing

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:39.454807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers