Chen Li

Barbican, London, UK

I am an Applied Scientist at SpiNNcloud Systems, working on neuromorphic computing, large language models(LLMs), and LLM-neuromorphic-hardware co-design. Previously, I was a Research Associate (Postdoc) at King’s College London (PI: Prof. Bipin Rajendran). I obtained my Ph.D. in Computer Science from the University of Manchester, supervised by Prof. Steve Furber.

My research interests include neuromorphic computing, spiking neural networks, analog in-memory computing, and large language models. I take a full-stack approach that spans algorithms, components, devices, and hardware adaptation and deployment for efficient, resilient AI. I am also interested in reinforcement learning and very new to Triton/tilelang kernel development.

selected publications

NCE

Efficient Transformer Adaptation for Analog In-Memory Computing via Low-Rank Adapters

Chen Li, Elena Ferro, Corey Lammie, and 3 more authors

Neuromorphic Computing and Engineering, 2026

Bib

@article{li2026efficient,
  title = {Efficient Transformer Adaptation for Analog In-Memory Computing via Low-Rank Adapters},
  author = {Li, Chen and Ferro, Elena and Lammie, Corey and Gallo, Manuel Le and Sebastian, Abu and Rajendran, Bipin},
  journal = {Neuromorphic Computing and Engineering},
  volume = {6},
  pages = {014011},
  year = {2026},
  publisher = {IOP Publishing},
}

IEDM

Heterogeneous Embedded Neural Processing Units Utilizing PCM-based Analog In-Memory Computing

Irem Boybat-Kara, Thomas Boesch, Michele Allegra, and 10 more authors

In IEEE International Electron Devices Meeting (IEDM), 2024

Bib

@inproceedings{boybat2024heterogeneous,
  title = {Heterogeneous Embedded Neural Processing Units Utilizing PCM-based Analog In-Memory Computing},
  author = {Boybat-Kara, Irem and Boesch, Thomas and Allegra, Michele and Fonseca-Guerra, Cezar and Tao, Xuechen and Oppermann, Julian and Papandreou, Nikos and Sebastian, Abu and Arnaud, Francois and Li, Chen and Rajendran, Bipin and Joshi, Vijay and Gallo, Manuel Le},
  booktitle = {IEEE International Electron Devices Meeting (IEDM)},
  year = {2024},
  organization = {IEEE},
}

ICCV

Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence

Chen Li, Edward Jones, and Steve Furber

In International Conference on Computer Vision (ICCV), 2023

Bib

@inproceedings{li2023unleashing,
  title = {Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence},
  author = {Li, Chen and Jones, Edward and Furber, Steve},
  booktitle = {International Conference on Computer Vision (ICCV)},
  pages = {1--10},
  year = {2023},
}

Front. Neurosci.

Quantization Framework for Fast Spiking Neural Networks

Chen Li, Lei Ma, and Steve B Furber

Frontiers in Neuroscience, 2022

Bib

@article{li2022quantization,
  title = {Quantization Framework for Fast Spiking Neural Networks},
  author = {Li, Chen and Ma, Lei and Furber, Steve B},
  journal = {Frontiers in Neuroscience},
  volume = {16},
  pages = {918793},
  year = {2022},
  publisher = {Frontiers},
}

Phys. Rev. Appl.

Nanoscale Room-Temperature Multilayer Skyrmionic Synapse for Deep Spiking Neural Networks

Runze Chen, Chen Li, Yu Li, and 5 more authors

Physical Review Applied, 2020

Bib

@article{chen2020nanoscale,
  title = {Nanoscale Room-Temperature Multilayer Skyrmionic Synapse for Deep Spiking Neural Networks},
  author = {Chen, Runze and Li, Chen and Li, Yu and Miles, James J and Indiveri, Giacomo and Furber, Steve and Pavlidis, Vasilis F and Moutafis, Christoforos},
  journal = {Physical Review Applied},
  volume = {14},
  number = {1},
  pages = {014096},
  year = {2020},
  publisher = {APS},
}