Julian Robledo Mejia |
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Phone Fax Visitor's Address |
+49 (0)351 463 43732 +49 (0)351 463 39995 Chair for Compiler Construction |
Julian Robledo Mejia received his Bachelor degree in Electronic Engineering from University of Antioquia (UdeA) in Medellín Colombia and his Master’s degree in Embedded Systems from the Polytechnic University of Turin in 2017 in Italy, his graduate thesis was about developing a fault injection environment for a real-time operating system. After finishing his master’s program, Julian worked in the automotive sector developing embedded software for major carmakers. In January 2020, Julian joined the Chair for Compiler Construction as research assistant.
Mobile networks are constantly evolving to support ever increasing data demands under increasing real-time constraints. 5G networks demands high data rate and ultra-low latency, as well as introduces a wide variety of new use cases resulting in a high workload heterogeneity.
My research focus area for meeting these demands is increasing flexibility of baseband systems to optimize performance and energy consumption of 5G networks. Some of the used methodologies include model-based optimizations, such as adaptive scheduling methodologies and adaptive dataflow models, applied to heterogeneous multi-core platforms.
If you are interested on these topics, do not hesitate to contact me.
2022
- Julian Robledo, Jeronimo Castrillon, "Parameterizable mobile workloads for adaptable base station optimizations", Proceedings of the IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-22), pp. 381-386, Dec 2022. [doi] [Bibtex & Downloads]
Parameterizable mobile workloads for adaptable base station optimizations
×Reference
Julian Robledo, Jeronimo Castrillon, "Parameterizable mobile workloads for adaptable base station optimizations", Proceedings of the IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-22), pp. 381-386, Dec 2022. [doi]
Abstract
Recent works on 5G baseband processing systems address the optimization of applications with different requirements of quality of service (QoS). The volume and heterogeneity of applications that have to be processed on a base station are growing and 5G introduces new use cases that push system designers towards more flexible and adaptable approaches. To investigate future network challenges of mobile communications, a good methodology for the generation of realistic workloads, that allows target optimizations of different traffic scenarios, is required. In this paper, we study the variation of real traffic data on multiple base stations and identify the main sources for the high variation of the 5G workloads. We propose a methodology for parameterizable workload generation for users with different QoS requirements that enables optimization techniques in baseband processing systems. We demonstrate the feasibility of our approach based on a virtual base station using a heterogeneous hardware model and various state-of-the-art mapping policies.
Bibtex
@InProceedings{robledo_mcsoc22,
author = {Julian Robledo and Jeronimo Castrillon},
booktitle = {Proceedings of the IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-22)},
title = {Parameterizable mobile workloads for adaptable base station optimizations},
doi = {10.1109/MCSoC57363.2022.00067},
url = {https://ieeexplore.ieee.org/document/10008473},
location = {Penang, Malaysia},
pages = {381--386},
abstract = {Recent works on 5G baseband processing systems address the optimization of applications with different requirements of quality of service (QoS). The volume and heterogeneity of applications that have to be processed on a base station are growing and 5G introduces new use cases that push system designers towards more flexible and adaptable approaches. To investigate future network challenges of mobile communications, a good methodology for the generation of realistic workloads, that allows target optimizations of different traffic scenarios, is required. In this paper, we study the variation of real traffic data on multiple base stations and identify the main sources for the high variation of the 5G workloads. We propose a methodology for parameterizable workload generation for users with different QoS requirements that enables optimization techniques in baseband processing systems. We demonstrate the feasibility of our approach based on a virtual base station using a heterogeneous hardware model and various state-of-the-art mapping policies.},
month = dec,
year = {2022},
}Downloads
2212_Robledo_MCSOC [PDF]
Permalink
2021
- Robert Khasanov, Julian Robledo, Christian Menard, Andr'es Goens, Jeronimo Castrillon, "Domain-specific hybrid mapping for energy-efficient baseband processing in wireless networks", In ACM Transactions on Embedded Computing Systems (TECS). Special issue of the International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES), Association for Computing Machinery, vol. 20, no. 5s, New York, NY, USA, Sep 2021. [doi] [Bibtex & Downloads]
Domain-specific hybrid mapping for energy-efficient baseband processing in wireless networks
×Reference
Robert Khasanov, Julian Robledo, Christian Menard, Andr'es Goens, Jeronimo Castrillon, "Domain-specific hybrid mapping for energy-efficient baseband processing in wireless networks", In ACM Transactions on Embedded Computing Systems (TECS). Special issue of the International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES), Association for Computing Machinery, vol. 20, no. 5s, New York, NY, USA, Sep 2021. [doi]
Abstract
Advancing telecommunication standards continuously push for larger bandwidths, lower latencies, and faster data rates. The receiver baseband unit not only has to deal with a huge number of users expecting connectivity but also with a high workload heterogeneity. As a consequence of the required flexibility, baseband processing has seen a trend towards software implementations in cloud Radio Access Networks (cRANs). The flexibility gained from software implementation comes at the price of impoverished energy efficiency. This paper addresses the trade-off between flexibility and efficiency by proposing a domain-specific hybrid mapping algorithm. Hybrid mapping is an established approach from the model-based design of embedded systems that allows us to retain flexibility while targeting heterogeneous hardware. Depending on the current workload, the runtime system selects the most energy-efficient mapping configuration without violating timing constraints. We leverage the structure of baseband processing, and refine the scheduling methodology, to enable efficient mapping of 100s of tasks at the millisecond granularity, improving upon state-of-the-art hybrid approaches. We validate our approach on an Odroid XU4 and virtual platforms with application-specific accelerators on an open-source prototype. On different LTE workloads, our hybrid approach shows significant improvements both at design time and at runtime. At design-time, mappings of similar quality to those obtained by state-of-the-art methods are generated around four orders of magnitude faster. At runtime, multi-application schedules are computed 37.7% faster than the state-of-the-art without compromising on the quality.
Bibtex
@Article{khasanov_cases21,
author = {Robert Khasanov and Julian Robledo and Christian Menard and Andrés Goens and Jeronimo Castrillon},
title = {Domain-specific hybrid mapping for energy-efficient baseband processing in wireless networks},
doi = {10.1145/3476991},
issn = {1539-9087},
number = {5s},
url = {https://doi.org/10.1145/3476991},
volume = {20},
abstract = {Advancing telecommunication standards continuously push for larger bandwidths, lower latencies, and faster data rates. The receiver baseband unit not only has to deal with a huge number of users expecting connectivity but also with a high workload heterogeneity. As a consequence of the required flexibility, baseband processing has seen a trend towards software implementations in cloud Radio Access Networks (cRANs). The flexibility gained from software implementation comes at the price of impoverished energy efficiency. This paper addresses the trade-off between flexibility and efficiency by proposing a domain-specific hybrid mapping algorithm. Hybrid mapping is an established approach from the model-based design of embedded systems that allows us to retain flexibility while targeting heterogeneous hardware. Depending on the current workload, the runtime system selects the most energy-efficient mapping configuration without violating timing constraints. We leverage the structure of baseband processing, and refine the scheduling methodology, to enable efficient mapping of 100s of tasks at the millisecond granularity, improving upon state-of-the-art hybrid approaches. We validate our approach on an Odroid XU4 and virtual platforms with application-specific accelerators on an open-source prototype. On different LTE workloads, our hybrid approach shows significant improvements both at design time and at runtime. At design-time, mappings of similar quality to those obtained by state-of-the-art methods are generated around four orders of magnitude faster. At runtime, multi-application schedules are computed 37.7% faster than the state-of-the-art without compromising on the quality.},
address = {New York, NY, USA},
articleno = {60},
issue_date = {October 2021},
journal = {ACM Transactions on Embedded Computing Systems (TECS). Special issue of the International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES)},
location = {Virtual conference},
month = sep,
numpages = {26},
publisher = {Association for Computing Machinery},
year = {2021},
}Downloads
2110_Khasanov_CASES [PDF]
Related Paths
Permalink
- Christian Menard, Andr'es Goens, Gerald Hempel, Robert Khasanov, Julian Robledo, Felix Teweleitt, Jeronimo Castrillon, "Mocasin—Rapid Prototyping of Rapid Prototyping Tools: A Framework for Exploring New Approaches in Mapping Software to Heterogeneous Multi-cores", Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools, co-located with 16th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC), Association for Computing Machinery, pp. 66–73, New York, NY, USA, Jan 2021. (Video Presentation) [doi] [Bibtex & Downloads]
Mocasin—Rapid Prototyping of Rapid Prototyping Tools: A Framework for Exploring New Approaches in Mapping Software to Heterogeneous Multi-cores
×Reference
Christian Menard, Andr'es Goens, Gerald Hempel, Robert Khasanov, Julian Robledo, Felix Teweleitt, Jeronimo Castrillon, "Mocasin—Rapid Prototyping of Rapid Prototyping Tools: A Framework for Exploring New Approaches in Mapping Software to Heterogeneous Multi-cores", Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools, co-located with 16th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC), Association for Computing Machinery, pp. 66–73, New York, NY, USA, Jan 2021. (Video Presentation) [doi]
Abstract
We present Mocasin, an open-source rapid prototyping framework for researching, implementing and validating new algorithms and solutions in the field of mapping software to heterogeneous multi-cores. In contrast to the many existing tools that often specialize for a particular use-case, Mocasin is an open, flexible and generic research environment that abstracts over the approaches taken by other tools. Mocasin is designed to support a wide range of models of computation and input formats, implements manifold mapping strategies and provides an adjustable high-level simulator for performance estimation. This infrastructure serves as a flexible vehicle for exploring new approaches and as a blueprint for building customized tools. We highlight the key design aspects of Mocasin that enable its flexibility and illustrate its capabilities in a case-study showing how Mocasin can be used for building a customized tool for researching runtime mapping strategies in an LTE uplink receiver.
Bibtex
@InProceedings{menard_rapido21,
author = {Christian Menard and Andrés Goens and Gerald Hempel and Robert Khasanov and Julian Robledo and Felix Teweleitt and Jeronimo Castrillon},
title = {Mocasin---Rapid Prototyping of Rapid Prototyping Tools: A Framework for Exploring New Approaches in Mapping Software to Heterogeneous Multi-cores},
booktitle = {Proceedings of the 2021 Drone Systems Engineering and Rapid Simulation and Performance Evaluation: Methods and Tools, co-located with 16th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC)},
year = {2021},
address = {New York, NY, USA},
month = jan,
publisher = {ACM},
doi = {10.1145/3444950.3447285},
isbn = {9781450389525},
location = {Budapest, Hungary},
pages = {66–73},
publisher = {Association for Computing Machinery},
series = {DroneSE and RAPIDO '21},
url = {https://doi.org/10.1145/3444950.3447285},
abstract = {We present Mocasin, an open-source rapid prototyping framework for researching, implementing and validating new algorithms and solutions in the field of mapping software to heterogeneous multi-cores. In contrast to the many existing tools that often specialize for a particular use-case, Mocasin is an open, flexible and generic research environment that abstracts over the approaches taken by other tools. Mocasin is designed to support a wide range of models of computation and input formats, implements manifold mapping strategies and provides an adjustable high-level simulator for performance estimation. This infrastructure serves as a flexible vehicle for exploring new approaches and as a blueprint for building customized tools. We highlight the key design aspects of Mocasin that enable its flexibility and illustrate its capabilities in a case-study showing how Mocasin can be used for building a customized tool for researching runtime mapping strategies in an LTE uplink receiver.},
numpages = {8},
}Downloads
2101_Menard_RAPIDO [PDF]
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