NAPEL: Near-memory computing application performance prediction via ensemble learning

Gagandeep Singh, Juan Gómez-Luna, Giovanni Mariani, Geraldo F. Oliveira, Stefano Corda, Sander Stuijk, Onur Mutlu, Henk Corporaal

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)

Abstract

The cost of moving data between the memory/storage units and the compute units is a major contributor to the execution time and energy consumption of modern workloads in computing systems. A promising paradigm to alleviate this data movement bottleneck is near-memory computing (NMC), which consists of placing compute units close to the memory/storage units. There is substantial research effort that proposesNMCarchitectures and identifiesworkloads that can benefit from NMC. System architects typically use simulation techniques to evaluate the performance and energy consumption of their designs. However, simulation is extremely slow, imposing long times for design space exploration. In order to enable fast early-stage design space exploration of NMC architectures, we need high-level performance and energy models. We present NAPEL, a high-level performance and energy estimation framework for NMC architectures. NAPEL leverages ensemble learning to develop a model that is based on microarchitectural parameters and application characteristics. NAPEL training uses a statistical technique, called design of experiments, to collect representative training data efficiently. NAPEL provides early design space exploration 220× faster than a state-of-the-artNMCsimulator, on average, with error rates of to 8.5% and 11.6% for performance and energy estimations, respectively, compared to the NMC simulator. NAPEL is also capable of making accurate predictions for previously-unseen applications.

LanguageEnglish
Title of host publicationProceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781450367257
DOIs
StatePublished - 2 Jun 2019
Event56th Annual Design Automation Conference, (DAC2019) - Las Vegas, United States
Duration: 2 Jun 20196 Jun 2019
https://dac.com/

Conference

Conference56th Annual Design Automation Conference, (DAC2019)
Abbreviated titleDAC2019
CountryUnited States
CityLas Vegas
Period2/06/196/06/19
Internet address

Fingerprint

Ensemble Learning
Performance Prediction
Data storage equipment
Computing
Design Space Exploration
Unit
Energy Consumption
Energy utilization
Energy Model
Design of Experiments
Performance Model
Energy
Leverage
Design of experiments
Execution Time
Workload
Error Rate
Simulation
Simulator
Simulators

Cite this

Singh, G., Gómez-Luna, J., Mariani, G., Oliveira, G. F., Corda, S., Stuijk, S., ... Corporaal, H. (2019). NAPEL: Near-memory computing application performance prediction via ensemble learning. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019 [a27] Institute of Electrical and Electronics Engineers. DOI: 10.1145/3316781.3317867
Singh, Gagandeep ; Gómez-Luna, Juan ; Mariani, Giovanni ; Oliveira, Geraldo F. ; Corda, Stefano ; Stuijk, Sander ; Mutlu, Onur ; Corporaal, Henk. / NAPEL : Near-memory computing application performance prediction via ensemble learning. Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers, 2019.
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abstract = "The cost of moving data between the memory/storage units and the compute units is a major contributor to the execution time and energy consumption of modern workloads in computing systems. A promising paradigm to alleviate this data movement bottleneck is near-memory computing (NMC), which consists of placing compute units close to the memory/storage units. There is substantial research effort that proposesNMCarchitectures and identifiesworkloads that can benefit from NMC. System architects typically use simulation techniques to evaluate the performance and energy consumption of their designs. However, simulation is extremely slow, imposing long times for design space exploration. In order to enable fast early-stage design space exploration of NMC architectures, we need high-level performance and energy models. We present NAPEL, a high-level performance and energy estimation framework for NMC architectures. NAPEL leverages ensemble learning to develop a model that is based on microarchitectural parameters and application characteristics. NAPEL training uses a statistical technique, called design of experiments, to collect representative training data efficiently. NAPEL provides early design space exploration 220× faster than a state-of-the-artNMCsimulator, on average, with error rates of to 8.5{\%} and 11.6{\%} for performance and energy estimations, respectively, compared to the NMC simulator. NAPEL is also capable of making accurate predictions for previously-unseen applications.",
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Singh, G, Gómez-Luna, J, Mariani, G, Oliveira, GF, Corda, S, Stuijk, S, Mutlu, O & Corporaal, H 2019, NAPEL: Near-memory computing application performance prediction via ensemble learning. in Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019., a27, Institute of Electrical and Electronics Engineers, 56th Annual Design Automation Conference, (DAC2019), Las Vegas, United States, 2/06/19. DOI: 10.1145/3316781.3317867

NAPEL : Near-memory computing application performance prediction via ensemble learning. / Singh, Gagandeep; Gómez-Luna, Juan; Mariani, Giovanni; Oliveira, Geraldo F.; Corda, Stefano; Stuijk, Sander; Mutlu, Onur; Corporaal, Henk.

Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers, 2019. a27.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - The cost of moving data between the memory/storage units and the compute units is a major contributor to the execution time and energy consumption of modern workloads in computing systems. A promising paradigm to alleviate this data movement bottleneck is near-memory computing (NMC), which consists of placing compute units close to the memory/storage units. There is substantial research effort that proposesNMCarchitectures and identifiesworkloads that can benefit from NMC. System architects typically use simulation techniques to evaluate the performance and energy consumption of their designs. However, simulation is extremely slow, imposing long times for design space exploration. In order to enable fast early-stage design space exploration of NMC architectures, we need high-level performance and energy models. We present NAPEL, a high-level performance and energy estimation framework for NMC architectures. NAPEL leverages ensemble learning to develop a model that is based on microarchitectural parameters and application characteristics. NAPEL training uses a statistical technique, called design of experiments, to collect representative training data efficiently. NAPEL provides early design space exploration 220× faster than a state-of-the-artNMCsimulator, on average, with error rates of to 8.5% and 11.6% for performance and energy estimations, respectively, compared to the NMC simulator. NAPEL is also capable of making accurate predictions for previously-unseen applications.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers

ER -

Singh G, Gómez-Luna J, Mariani G, Oliveira GF, Corda S, Stuijk S et al. NAPEL: Near-memory computing application performance prediction via ensemble learning. In Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers. 2019. a27. Available from, DOI: 10.1145/3316781.3317867