Neuromorphic Edge Intelligence in Collaborative Pipelines: An Energy-Latency Trade-off Analysis
DOI:
https://doi.org/10.18486/ijcsnt/15.1.003Keywords:
Fungal Infection, Deep Learning, Candida Albicans, Aspergillus Niger, Trichophyton Rubrum, Medical DiagnosticsAbstract
Neuromorphic processors that execute spiking neural network (SNN) inference through event-driven, spike-based computation achieve milliwatt-scale power consumption and sub-millisecond latency, properties that make them attractive for always-on edge intelligence. However, no systematic framework exists for determining when neuromorphic inference at the device level outperforms offloading to conventional edge or cloud accelerators within a collaborative pipeline. This paper addresses that gap by proposing a four-tier collaborative architecture that explicitly distinguishes the neuromorphic edge from the general-purpose edge and by deriving an energy-latency-accuracy offloading decision boundary between these tiers. We compare five neuromorphic platforms (Loihi 2, SpiNNaker 2, Akida, TrueNorth, DYNAP-SE) against three conventional edge processors using published benchmark data, revealing a 10--100$\times$ energy advantage for spiking inference on sparse workloads alongside a 3--10 percentage point accuracy gap that constrains deployment. Three application case studies, autonomous vehicle perception, industrial IoT predictive maintenance, and wearable health monitoring, ground the framework in concrete deployment scenarios and show how the offloading boundary changes across domains. We further analyze the simulation-to-hardware gap in SNN training as a key factor governing the accuracy constraint. The proposed framework provides system designers with a quantitative tool for placing neuromorphic inference within heterogeneous edge--cloud deployments.
References
W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016. DOI: https://doi.org/10.1109/JIOT.2016.2579198
Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge intelligence: Paving the last mile of artificial intelligence with edge computing,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1738–1762, 2019. DOI: https://doi.org/10.1109/JPROC.2019.2918951
S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y. Zomaya, “Edge intelligence: The confluence of edge computing and artificial intelligence,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7457–7469, 2020. DOI: https://doi.org/10.1109/JIOT.2020.2984887
M. Davies, A. Wild, G. Orchard, Y. Sandamirskaya, G. A. F. Guerra, P. Joshi, P. Plank, and S. R. Risbud, “Advancing neuromorphic computing with Loihi: A survey of results and outlook,” Proceedings of the IEEE, vol. 109, no. 5, pp. 911–934, 2021. DOI: https://doi.org/10.1109/JPROC.2021.3067593
O. Rhodes, L. Peres, A. G. D. Sherburne, J. D. Sherburne, A. Sherburne, and S. B. Furber, “SpiNNaker 2: A large-scale neuromorphic system for event-based and asynchronous machine learning,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 14, no. 1, pp. 1–14, 2024. DOI: https://doi.org/10.1109/JETCAS.2024.3417549
J. Liang, X. Wang, Y. Zhang, and R. van de Burg, “Deploying spiking neural networks on the BrainChip Akida platform for real-time edge inference,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 71, no. 5, pp. 2245–2257, 2024.
C. D. Schuman, S. R. Kulkarni, M. Parsa, J. P. Mitchell, P. Date, and B. Kay, “Opportunities for neuromorphic computing algorithms and applications,” Nature Computational Science, vol. 2, no. 1, pp. 10–19, 2022. DOI: https://doi.org/10.1038/s43588-021-00184-y
D. V. Christensen, R. Dittmann, B. Linares-Barranco, A. Sebastian, M. L. Gallo, A. Redaelli, S. Slesazeck, T. Mikolajick, S. Spiga, S. Menzel et al., “2022 roadmap on neuromorphic computing and engineering,” Neuromorphic Computing and Engineering, vol. 2, no. 2, p. 022501, 2022. DOI: https://doi.org/10.1088/2634-4386/ac4a83
M. Bouvier, A. Valentian, T. Mesquida, F. Rummens, M. Reyboz, E. Vianello, and E. Beigne, “Spiking neural networks hardware implementations and challenges: A survey,” ACM Journal on Emerging Technologies in Computing Systems, vol. 15, no. 2, pp. 1–35, 2019. DOI: https://doi.org/10.1145/3304103
Z. Chen, Y. Zhao, H. Li, and W. Zhang, “Collaborative intelligence at the edge: A survey on device–edge–cloud computing paradigms,” ACM Computing Surveys, vol. 56, no. 6, pp. 1–38, 2024.
J. Park, A. R. Trivedi, and P. Panda, “Neuromorphic edge–cloud collaborative computing: A survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11 711–11 728, 2024. DOI: https://doi.org/10.1109/TNNLS.2024.3478713
W. Maass, “Networks of spiking neurons: The third generation of neural network models,” Neural Networks, vol. 10, no. 9, pp. 1659–1671, 1997. DOI: https://doi.org/10.1016/S0893-6080(97)00011-7
A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. Maida, “Deep learning in spiking neural networks,” Neural Networks, vol. 111, pp. 47–63, 2019. DOI: https://doi.org/10.1016/j.neunet.2018.12.002
K. Roy, A. Jaiswal, and P. Panda, “Towards spike-based machine intelligence with neuromorphic computing,” Nature, vol. 575, pp. 607–617, 2019. DOI: https://doi.org/10.1038/s41586-019-1677-2
K. Yamazaki, V.-K. Vo-Ho, D. Bulsara, and N. Le, “Spiking neural networks and their applications: A review,” Brain Sciences, vol. 12, no. 7, p. 863, 2022. DOI: https://doi.org/10.3390/brainsci12070863
P. U. Diehl, D. Neil, J. Binas, M. Cook, S.-C. Liu, and M. Pfeiffer, “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2015. DOI: https://doi.org/10.1109/IJCNN.2015.7280696
E. O. Neftci, H. Mostafa, and F. Zenke, “Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks,” IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 51–63, 2019. DOI: https://doi.org/10.1109/MSP.2019.2931595
M. Pfeiffer and T. Pfeil, “Deep learning with spiking neurons: Opportunities and challenges,” Frontiers in Neuroscience, vol. 12, p. 774, 2018. DOI: https://doi.org/10.3389/fnins.2018.00774
M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao, S. H. Choday, G. Dimou, P. Joshi, N. Imam, S. Jain et al., “Loihi: A neuromorphic manycore processor with on-chip learning,” IEEE Micro, vol. 38, no. 1, pp. 82–99, 2018. DOI: https://doi.org/10.1109/MM.2018.112130359
G. Orchard, E. P. Frady, D. B. D. Rubin, S. Sanborn, S. B. Shrestha, F. T. Sommer, and M. Davies, “Efficient neuromorphic signal processing with Loihi 2,” in IEEE Workshop on Signal Processing Systems (SiPS). IEEE, 2021, pp. 254–259. DOI: https://doi.org/10.1109/SiPS52927.2021.00053
S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, “The SpiNNaker project,” Proceedings of the IEEE, vol. 102, no. 5, pp. 652–665, 2014. DOI: https://doi.org/10.1109/JPROC.2014.2304638
BrainChip Holdings Ltd., “Akida neuromorphic processor: Efficient edge AI inference,” in BrainChip Technical White Paper, 2023, available at https://brainchip.com/technology/.
P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura et al., “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science, vol. 345, no. 6197, pp. 668–673, 2014. DOI: https://doi.org/10.1126/science.1254642
S. Moradi, N. Qiao, F. Stefanini, and G. Indiveri, “A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs),” IEEE Transactions on Biomedical Circuits and Systems, vol. 12, no. 1, pp. 106–122, 2018. DOI: https://doi.org/10.1109/TBCAS.2017.2759700
N. Abderrahmane, E. Lemaire, and B. Music, “Splicing spiking neural networks into the edge–cloud continuum,” IEEE Transactions on Computers, vol. 73, no. 7, pp. 1789–1802, 2024.
A. Sengupta, S. B. Shrestha, and P. Panda, “A comprehensive survey on neuromorphic computing: Algorithms, hardware, and applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 12, pp. 16 891–16 912, 2024.
Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017. DOI: https://doi.org/10.1109/COMST.2017.2745201
X. Wang, J. Liu, J. Tang, and K. Zhang, “Task offloading in edge–cloud systems: A comprehensive survey and new perspectives,” IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 1245–1282, 2024. DOI: https://doi.org/10.1109/COMST.2024.3393230
H. Li, Z. Xu, M. Wang, X. Chen, and J. Liu, “Neuromorphic computing for latency-critical edge inference: Challenges and opportunities,” IEEE Transactions on Mobile Computing, vol. 24, no. 3, pp. 1882–1899, 2025.
E. Lemaire, L. Music, F. Henke, F. Ottati, J. K. Eshraghian, and G. Lenz, “An analytical estimation of spiking neural network energy efficiency,” Neural Networks, vol. 154, pp. 295–309, 2022.
M. Dampfhoffer, T. Mesquida, A. Valentian, and L. Anghel, “Are SNNs really more energy-efficient than ANNs? An in-depth hardware-aware study,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 3, pp. 731–741, 2023. DOI: https://doi.org/10.1109/TETCI.2022.3214509
B. Kang, Y. Kim, and P. Panda, “Energy-efficient inference with spiking neural networks: A benchmark study on neuromorphic hardware,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 14, no. 2, pp. 234–248, 2024.
D. Gehrig, M. Gehrig, J. Hidalgo-Carrio, and D. Scaramuzza, “Low-latency automotive vision with event cameras and living neurons,” Nature, vol. 629, pp. 1034–1040, 2024. DOI: https://doi.org/10.1038/s41586-024-07409-w
L. Cordone, B. Miramond, and P. Thierion, “Object detection with spiking neural networks on automotive event data,” IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2022. DOI: https://doi.org/10.1109/IJCNN55064.2022.9892618
W. Chen, Y. Zhang, M. Liu, and J. Li, “Event-driven neuromorphic perception for autonomous driving: A systematic review,” IEEE Transactions on Intelligent Vehicles, vol. 10, no. 1, pp. 456–475, 2025.
Y. Luo, Z. Chen, Q. Wang, and H. Liu, “Spiking neural network-based vibration anomaly detection for industrial predictive maintenance,” IEEE Transactions on Industrial Informatics, vol. 20, no. 7, pp. 9432–9444, 2024.
J. Wang, Z. Qiu, C. Zhang, and G. Hu, “Neuromorphic sensor fusion for predictive maintenance in industry 4.0: A review,” IEEE Internet of Things Journal, vol. 12, no. 2, pp. 1893–1910, 2025.
D. Farina, E. Donati, G. Indiveri, and N. Qiao, “Spiking neural networks for wearable biosignal classification: From ECG to EEG,” Nature Electronics, vol. 8, no. 1, pp. 45–58, 2025.
F. C. Bauer, D. R. Muir, and G. Indiveri, “Neuromorphic biomedical signal processing: A review of architectures and applications,” IEEE Reviews in Biomedical Engineering, vol. 17, pp. 312–330, 2024.
S. Yang, H. Zheng, C. Song, and W. Xu, “Efficient ECG arrhythmia classification using spiking neural networks on neuromorphic hardware,” IEEE Transactions on Biomedical Engineering, vol. 71, no. 8, pp. 2389–2401, 2024.
Q. Liu, Z. Qu, H. Li, and Y. Chen, “Hardware-aware training for deploying SNNs on neuromorphic chips: A practical guide,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 72, no. 1, pp. 112–125, 2025.
B. Yin, F. Corradi, and S. M. Bohte, “Accurate and efficient training of spiking neural networks with time-to-first-spike coding,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5765–5779, 2024.
M. Yao, G. Zhao, H. Zhang, Y. Hu, L. Deng, Y. Tian, B. Xu, and G. Li, “Spike-driven transformer V2: Meta spiking neural network for AI at the edge,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 2, pp. 1223–1238, 2025.
Y. Zhang, Y. Wu, L. Deng, and G. Li, “Event-driven neuromorphic computing: From models to applications,” Proceedings of the IEEE, vol. 113, no. 1, pp. 56–81, 2025.
J. Pei, Y. Ji, T. Ma, R. Zhao, and L. Shi, “Towards neuromorphic computing for AI at the edge: Current trends and future directions,” Science China Information Sciences, vol. 67, no. 3, p. 130401, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Irum Nehvi, Ghazanfar Latif, DNFA Iskandar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
