Just another paper confirming the future of neuromorphic computing. The paper does not refer to Brainchip or AKIDA as it is authored by members of the Intel Neuromorphic Research Community however as we know Loihi 2 is not a commercial version of Intel's neuromorphic vision and suffers with a number of problems including a significant degree of difficulty should you wish to program it the only viable commercial alternative is AKIDA technology. We also know that ESA has recently Selected EDGX-AKIDA to develop an in space computer the reality of Brainchip's commercial lead is already in evidence publicly.
Importantly this fully supports the future that Brainchip's AKIDA technology has where the need for cognitive communication is becoming essential to meet the needs of over burdened bandwidth and latency both on Earth and in Space:
https://ieeexplore.ieee.org/iel7/9882533/10070384/10387580.pdf
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
FLOR ORTIZ 1 (Member, IEEE), NICOLAS SKATCHKOVSKY2 (Member, IEEE),
EVA LAGUNAS 1 (Senior Member, IEEE),
WALLACE A. MARTINS 1,3 (Senior Member, IEEE), GEOFFREY EAPPEN 1 (Member, IEEE), SAED DAOUD1 (Member, IEEE), OSVALDO SIMEONE 4 (Fellow, IEEE),
BIPIN RAJENDRAN 4 (Senior Member, IEEE),
AND SYMEON CHATZINOTAS 1 (Fellow, IEEE)
1Interdisciplinary Centre for Security, Reliability, and Trust (SnT), 1855 Luxembourg City, Luxembourg 2Francis Crick Institute, NW1 1AT London, U.K.
3ISAE-SUPAERO, Université de Toulouse, 31055 Toulouse, France
4Department of Engineering, King’s College London, WC2R 2LS London, U.K.
(Flor Ortiz and Nicolas Skatchkovsky contributed equally to this work.) CORRESPONDING AUTHOR: F. ORTIZ (
flor.ortiz@uni.lu)
This work was supported in part by the European Space Agency (ESA)-The Application of Neuromorphic Processors to Satcom Applications under Grant 4000137378/22/UK/ND; and in part by the Luxembourg National Research Fund (FNR) through the project SmartSpace under Grant C21/IS/16193290. The work of Osvaldo Simeone was supported in part by the European Union’s Horizon Europe Project CENTRIC under Grant 101096379; in part by the Open Fellowship of the EPSRC under Grant EP/W024101/1; and in part by the Project REASON, a U.K. Government funded project through the Future Open Networks Research Challenge (FONRC) sponsored by the Department of Science Innovation and Technology (DSIT). The work of Bipin Rajendran was supported in part by the European Union’s Horizon Europe Project CENTRIC under Grant 101096379 and in part by the Open Fellowship of the EPSRC under Grant EP/X011356/1.
ABSTRACT The latest Satellite Communication (SatCom) missions are characterized by a fully reconfig- urable on-board software-defined payload, capable of adapting radio resources to the temporal and spatial variations of the system traffic. As pure optimization-based solutions have shown to be computationally tedious and to lack flexibility, Machine Learning (ML)-based methods have emerged as promising alter- natives. We investigate the application of energy-efficient brain-inspired ML models for on-board radio resource management. Apart from software simulation, we report extensive experimental results leveraging the recently released Intel Loihi 2 chip. To benchmark the performance of the proposed model, we implement conventional Convolutional Neural Networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. Most notably, for relevant workloads, Spiking Neural Networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100× as compared to the CNN-based reference platform. Our findings point to the significant potential of neuromorphic computing and SNNs in supporting on-board SatCom operations, paving the way for enhanced efficiency and sustainability in future SatCom systems.
INDEX TERMS Energy-efficient, neuromorphic computing, radio resource management, satellite communication, spiking neural networks
VII. CONCLUSION
SNNs excel in processing sparse temporal data due to their reliance on spike patterns. However, they face limitations with dense datasets common across domains, hindering their universal adoption over CNNs. SNNs energy efficiency benefits rely on specialized neuromorphic hardware, yet its limited availability poses a barrier to widespread implementation. Furthermore, the learning curve associated with neuromorphic computing, its evolving frameworks, and tools contrasts with the mature ecosystem surrounding CNNs. Despite these challenges, ongoing research and technological advancements offer promise for neuromorphic computing to complement traditional methods, paving the way for more energy-efficient processing in the future.
This article presents an extensive investigation into the benefits of incorporating neuromorphic computing and SNNs for on-board radio resource management in SatCom sys- tems. By leveraging innovative approaches, we addressed the challenge of implementing on-board RRM, comparing the performance of the proposed neuromorphic computing approach with a traditional CNN model. Our experiments demonstrate that SNNs, enabled by dedicated hardware, offer higher accuracy and significantly reduce energy consumption and latency. These remarkable results underscore the potential of neuromorphic computing and SNNs in improving RRM for SatCom, leading to better efficiency and sustainability for future SatCom systems.
To advance this research further, several avenues of investigation remain open. An important aspect is the imple- mentation of the proposed approach in a real system, taking into account factors such as radiation tolerance, which holds great significance in the space environment. Moreover, future research could focus on optimizing the SNN architecture to achieve better performance and energy efficiency, considering the specific requirements and constraints of SatCom systems.
Although our current model is suitable for GEO satellite systems [50], we recognize the dynamic nature of LEO/MEO systems [51], [52], where the Doppler effect, rapid elevation angle changes, and other factors significantly influence the analysis. In future work we intend to extend our model to address these challenges, recognizing that channel and traffic conditions vary much more rapidly due to the higher relative velocity of LEO/MEO satellites. This will require a more complex and robust model to accommodate the highly dynamic environment.
We also wish to emphasize the generalization perfor- mance of the proposed models. Rigorous testing through cross-validation on diverse datasets simulating various opera- tional scenarios has shown that the SNN model, in particular, exhibits strong generalization capabilities. This is evidenced by its ability to maintain high accuracy and low energy consumption when exposed to unseen data, indicating its robustness in real-world deployments. We acknowledge, however, that substantial changes in the operational environ- ment, such as a transition from GEO to LEO/MEO systems, will necessitate the adaptation of the model. To this end, our future work will focus on enhancing the model complexity to cope with the increased dynamics of LEO/MEO systems, ensuring that the generalization capabilities extend across different orbital conditions. The planned integration of con- tinual learning mechanisms is anticipated to bolster the model adaptability further, allowing it to update its parameters in response to evolving traffic patterns and channel condi- tions, thereby sustaining high performance without frequent retraining. These enhancements will be pivotal for deploying SNN-based RRM in the highly variable and demanding environment of space communications.
The findings of this study lay a solid foundation for the application of neuromorphic computing and SNNs in the field of SatCom RRM. Future investigations can build upon this work to further advance the state-of-the-art in SatCom systems, leveraging the benefits and insights gained from this comprehensive study.
ACKNOWLEDGMENT
The authors gratefully acknowledge the support of Intel Labs through the Intel Neuromorphic Research Community (INRC) and Tomas Navarro as ESA Officer. Please note that the views of the authors of this article do not necessarily reflect the views of ESA”
My opinion only DYOR
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