Boxun Xu

4164 Harold Frank Hall
Santa Barbara, CA 93106
I’m a third-year PhD candidate in Electrical and Computer Engineering at UC Santa Barbara, advised by Prof.Peng Li (Fellow of IEEE). My research interest focuses on the intersection of machine learning and computer architecture. Specifically, Brain-inspired Machine Learning, Efficient ML/LLM computing systems and Multimodal generation. I received Best Paper Award Nomination at ICCAD 2024. I also interned at Meta in 2024 and 2025.
Before this, I received my M.S. degree in Electrical and Computer Engineering from University of Michigan, Ann Arbor, advised by Prof.David Blaauw (Fellow of IEEE) and Prof.Dennis Sylvester (Fellow of IEEE), and B.S. in Electronic Engineering from University of Electronic Science and Technology of China, advised by Prof.Bei Yu at CUHK and Prof.Udo Schwingenschlögl at KAUST.
I’m actively looking for research discussion and collaboration focused on Generative Model’s efficiency issues and general applications. Feel free to reach out and keep in touch for future opportunities!
news
Jun 30, 2025 | One paper is accepted by ICCAD 2025! |
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May 23, 2025 | One paper is accepted by ITC 2025! |
Apr 29, 2025 | One paper is accepted by ASAP 2025! |
Mar 21, 2025 | One paper is accepted by International Symposium on Computer Architecture (ISCA’25)! See you in Tokyo! |
Jan 18, 2025 | Our Work “SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks” has been accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD) as a long paper! |
Jan 03, 2025 | This summer, I will join ![]() |
Oct 26, 2024 | Our Work “Spiking Transformer Accelerators in 3D Integration” is nominated as William J. McCalla Best Paper Award at ICCAD’24! |
Jul 01, 2024 | Two papers are accepted by ICCAD 2024! |
Jun 24, 2024 | I started my summer internship at ![]() |
May 25, 2024 | One paper is accepted by JSSC. |
selected publications
- Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard NegativesIn Arxiv, 2025
- 3D Acceleration for Mixture-of-Experts and Multi-Head Attention Spiking Transformers with Dynamic Head PruningIn ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2025
- Transfer Learning for Minimum Operating Voltage Prediction in Advanced Technology Nodes: Leveraging Legacy Data and Silicon Odometer SensingIn ACM/IEEE International Test Conference (ITC), 2025
- Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization SearchIn IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2025
- Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained PruningIn International Symposium on Computer Architecture (ISCA), 2025
- SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural NetworksIn IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems(TCAD), 2025
- AIMMI: Audio and Image Multi-Modal Intelligence via a Low-Power SoC With 2-MByte On-Chip MRAM for IoT DevicesIn IEEE Journal of Solid-State Circuits(JSSC), 2024
- Spiking Transformer Hardware Accelerators in 3D IntegrationIn ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2024
- DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal AttentionIn TMLR under review, 2024
- ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language ModelsIn ACM/IEEE International Conference on Computer-Aided Design (ICCAD), 2024
- Audio and Image Cross-Modal Intelligence via a 10TOPS/W 22nm SoC with Back-Propagation and Dynamic Power GatingIn 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI-Symposium), 2022