Publications

A. Pierro, J. Timcheck, J. Yik, M. Lindauer, E. Hüllermeier, M. Wever, “Evolutionary Mapping of Neural Networks to Spatial Accelerators,” Arxiv preprint, 2026. https://arxiv.org/abs/2602.04717

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J. Timcheck, A. Pierro, S. B. Shrestha, “A Compute and Communication Runtime Model for Loihi 2,” Arxiv preprint, 2026. https://arxiv.org/abs/2601.10035

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B. Meszaros, J. C. Knight, J. Timcheck, and T. Nowotny, “A Complete Pipeline for Deploying SNNs with Synaptic Delays on Loihi 2,” in Proceedings of the International Conference on Neuromorphic Systems (ICONS), 2025. https://arxiv.org/abs/2510.13757

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A. Pierro, S. Abreu, J. Timcheck, P. Stratmann, A. Wild, and S. B. Shrestha, “Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity,” Forty-second International Conference on Machine Learning (ICML), 2025. https://openreview.net/forum?id=UNrfYfbLZ3

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T. Shoesmith, J. C. Knight, B. Meszaros, J. Timcheck, and T. Nowotny, “Eventprop training for efficient neuromorphic applications,” 2025 Neuro Inspired Computational Elements (NICE), 2025. https://doi.org/10.1109/NICE65350.2025.11064940

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J. Yik, …, J. Timcheck et al., “The neurobench framework for benchmarking neuromorphic computing algorithms and systems,” Nature communications, 2025. https://www.nature.com/articles/s41467-025-56739-4

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S. M. Meyer, …, J. Timcheck et al., “A diagonal structured state space model on loihi 2 for efficient streaming sequence processing,” in 2025 Neuro Inspired Computational Elements (NICE), IEEE, 2025. https://ieeexplore.ieee.org/abstract/document/11065663/

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S. B. Shrestha, J. Timcheck, P. Frady, L. Campos-Macias, and M. Davies, “Efficient video and audio processing with loihi 2,” 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024. https://ieeexplore.ieee.org/abstract/document/10448003/

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J. Timcheck et al., “The Intel neuromorphic DNS challenge,” Neuromorphic Computing and Engineering, 2023. https://doi.org/10.1088/2634-4386/ace737

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C. Mackin, G. Burr, and J. P. Timcheck, “Translating artificial neural network software weights to hardware-specific analog conductances,” US20230105568A1, 2023. https://patents.google.com/patent/US20230105568A1/en

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J. Timcheck, J. Kadmon, K. Boahen, and S. Ganguli, “Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays,” PLoS computational biology, 2022. https://doi.org/10.1371/journal.pcbi.1010593

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C. Mackin, …, J. Timcheck et al., “Optimised weight programming for analogue memory-based deep neural networks,” Nature communications, 2022. https://www.nature.com/articles/s41467-022-31405-1

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J. Kadmon, J. Timcheck, and S. Ganguli, “Predictive coding in balanced neural networks with noise, chaos and delays,” Advances in neural information processing systems, 2020. https://proceedings.neurips.cc/paper/2020/hash/c236337b043acf93c7df397fdb9082b3-Abstract.html

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C. Clement, D. Drain, J. Timcheck, A. Svyatkovskiy, and N. Sundaresan, “Pymt5: multi-mode translation of natural language and python code with transformers,” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020. https://aclanthology.org/2020.emnlp-main.728/

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S. Chatrchyan, …, J. Timcheck, et al., “Search for the standard model Higgs boson produced in association with a top-quark pair in pp collisions at the LHC,” Journal of High Energy Physics, 2013. https://doi.org/10.1007/JHEP05(2013)145