Changchun Zhou
Orcid: 0009-0005-3968-5048Affiliations:
- Duke University, Center for Computational Evolutionary Intelligence (DCEI), Durham, NC, USA
According to our database1,
Changchun Zhou
authored at least 12 papers
between 2021 and 2025.
Collaborative distances:
Collaborative distances:
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Online presence:
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on orcid.org
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on github.com
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Bibliography
2025
An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.
IEEE Trans. Biomed. Circuits Syst., April, 2025
A Two-Stage Prediction + Detection Framework for Real-Time Epileptic Seizure Monitoring.
IEEE Trans. Instrum. Meas., 2025
Biomed. Signal Process. Control., 2025
23.4 Nebula: A 28nm 109.8TOPS/W 3D PNN Accelerator Featuring Adaptive Partition, Multi-Skipping, and Block-Wise Aggregation.
Proceedings of the IEEE International Solid-State Circuits Conference, 2025
Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-Aware Cache Compression.
Proceedings of the 52nd Annual International Symposium on Computer Architecture, 2025
2024
SoftAct: A High-Precision Softmax Architecture for Transformers Supporting Nonlinear Functions.
IEEE Trans. Circuits Syst. Video Technol., September, 2024
Adjustable Multi-Stream Block-Wise Farthest Point Sampling Acceleration in Point Cloud Analysis.
IEEE Trans. Circuits Syst. II Express Briefs, July, 2024
An Energy-Efficient Configurable Coprocessor Based on 1-D CNN for ECG Anomaly Detection.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2024
2023
CNN Accelerator at the Edge With Adaptive Zero Skipping and Sparsity-Driven Data Flow.
IEEE Trans. Circuits Syst. Video Technol., December, 2023
Sagitta: An Energy-Efficient Sparse 3D-CNN Accelerator for Real-Time 3-D Understanding.
IEEE Internet Things J., 2023
An Energy-Efficient 3D Point Cloud Neural Network Accelerator With Efficient Filter Pruning, MLP Fusion, and Dual-Stream Sampling.
Proceedings of the IEEE/ACM International Conference on Computer Aided Design, 2023
2021
An Energy-Efficient Low-Latency 3D-CNN Accelerator Leveraging Temporal Locality, Full Zero-Skipping, and Hierarchical Load Balance.
Proceedings of the 58th ACM/IEEE Design Automation Conference, 2021