DeepHEC: Hybrid Error Coding using Deep Learning

Pereira, Pablo Gil; Schmidt, Andreas; Herfet, Thorsten

doi:10.1109/edcc57035.2022.00015

Cited by 2 publications

(5 citation statements)

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“…Finally, although the graph search makes a slight difference for k opt , it does not make any significant difference for k range , which further supports the design decision of opting for a suboptimal but faster scheduler. SHARQ [24], Fast Search [23], and DeepHEC [23].…”

Section: The Cost Of Optimalitymentioning

confidence: 99%

“…The results here presented show that modeling the problem purely with deep learning results in excesively large models. DeepHEC learns (k, p, N C ), and hence needs larger neural networks to achieve similar performance in terms of supported coding configurations (5 hidden layers and 250 neurons per layer, see [23]). DeepSHARQ halves the inference time in comparison to DeepHEC, and its tail delay is an order of magnitude smaller.…”

Section: Inference Timementioning

confidence: 99%

“…Fig.12compares DeepSHARQ's inference time with three previously published algorithms: SHARQ[24], Fast Search[23], and DeepHEC[23]. Although Fast Search outperforms any other model in 43% of the cases, it also has a tail delay 6 orders of magnitude higher than DeepSHARQ's.…”

mentioning

confidence: 92%

“…Instead, it is a search problem with a complexity dependent on its input parameters-e.g., a linear increase in round-trip time leads to a more than linear increase of configurations to evaluate. Executing the search for realistic channel parameters on realistic CPS computing devices proved intractable [23].…”

Section: Introductionmentioning

confidence: 99%

“…Based on an efficient, but still intractable, reimplementation of the full search [21], we set out to bring hybrid error coding to resourceconstrained devices. In one branch, we approached the problem using machine learning [23], in particular using supervised learning with deep neural networks. In a second branch, we have been successful in decomposing the search problem in stages and improving individual stages algorithmically-achieving optimal redundancy efficiency but shorter inference time [24].…”

Section: Introductionmentioning

confidence: 99%

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DeepSHARQ: Hybrid Error Coding using Deep Learning

Pereira¹,

Vogelgesang²,

Miodek³

et al. 2023

Preprint

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Purpose: Cyber-Physical Systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactiveschemes cannot always fulfill application demands or have suboptimal performance. However, findingoptimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—underchanging channel conditions is a computationally challenging task. Methods: Recently, the systems community has started addressing these sorts of problems usinghybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts ofthe problem and learning-based approaches for parts that must be estimated (either for reasons ofuncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work andlimit the learning problem to block length prediction, the major contributor to inference time (andits variation) when searching for hybrid error coding configurations. The remaining parameters arefound algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. Results: DeepSHARQ applies block length regularization in order to reduce the neural networksin comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerningthe channel efficiency of coding configurations it generates, as it is trained so deviations from theoptimum are upper-bound by a configurable percentage. Additionally, DeepSHARQ is capable ofreacting to channel changes in real-time, thereby enabling Cyber-Physical Systems even on resource-constrained platforms. Conclusion: Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ toreact to channel changes faster and with a more predictable time than solutions that rely only oneither of the two approaches.

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Section: The Cost Of Optimalitymentioning

confidence: 99%

Section: Inference Timementioning

confidence: 99%

mentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

DeepSHARQ: Hybrid Error Coding using Deep Learning

Pereira¹,

Vogelgesang²,

Miodek³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

DeepSHARQ: hybrid error coding using deep learning

Pereira

Vogelgesang

Miodek

et al. 2023

J Reliable Intell Environ

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Cyber-physical systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactive schemes cannot always fulfill application demands or have suboptimal performance. However, finding optimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—under changing channel conditions is a computationally challenging task. Recently, the systems community has started addressing these sorts of problems using hybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts of the problem and learning-based approaches for parts that must be estimated (either for reasons of uncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work and limit the learning problem to block length prediction, the major contributor to inference time (and its variation) when searching for hybrid error coding configurations. The remaining parameters are found algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. DeepSHARQ applies block length regularization in order to reduce the neural networks in comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerning the channel efficiency of coding configurations it generates, as it is trained so deviations from the optimum are upper bound by a configurable percentage. In addition, DeepSHARQ is capable of reacting to channel changes in real time, thereby enabling cyber-physical systems even on resource-constrained platforms. Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ to react to channel changes faster and with a more predictable time than solutions that rely only on either of the two approaches.

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DeepHEC: Hybrid Error Coding using Deep Learning

Cited by 2 publications

References 0 publications

DeepSHARQ: Hybrid Error Coding using Deep Learning

DeepSHARQ: Hybrid Error Coding using Deep Learning

DeepSHARQ: hybrid error coding using deep learning

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