Kunming SHAO

Kunming SHAO

PhD candidate at HKUST

Publications

A Flexible Precision Scaling Deep Neural Network Accelerator with Efficient Weight Combination

Published in ISCAS, 2025

This paper presents a reconfigurable DNN accelerator for continuous activation/weight precisions with efficient weight combination.

Recommended citation: Liang Zhao*, Kunming Shao*, Fengshi Tian, et al. A Flexible Precision Scaling Deep Neural Network Accelerator with Efficient Weight Combination. In 2025 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1–6. IEEE, 2025. https://arxiv.org/abs/2502.00687

E-NPU: A 34~126nJ/Class Event-Driven Adaptive Neural SoC with Signal-Dynamics-Aware Feature Clustering and Multi-model In-Memory Inference/Training for Personalized Medical Wearables

Published in CICC, 2025

This paper presents an event-driven adaptive neural SoC with signal-dynamics-aware feature clustering and multi-model in-memory inference/training for personalized medical wearables.

Recommended citation: Fengshi Tian*, Jinbo Chen*, Kunming Shao*, et al. E-NPU: A 34~126nJ/Class Event-Driven Adaptive Neural SoC with Signal-Dynamics-Aware Feature Clustering and Multi-model In-Memory Inference/Training for Personalized Medical Wearables. In 2024 IEEE Custom Integrated Circuits Conference (CICC), pages 1–2. IEEE, 2025. To be released later

SynDCIM: A Performance-Aware Digital Computing-in-Memory Compiler with Multi-Spec-Oriented Subcircuit Synthesis

Published in DATE, 2025

This paper presents a performance-aware DCIM compiler with multi-spec-oriented subcircuit synthesis.

Recommended citation: Kunming Shao*, Fengshi Tian*, Xiaomeng Wang, et al. SynDCIM: A Performance-Aware Digital Computing-in-Memory Compiler with Multi-Spec-Oriented Subcircuit Synthesis. In 2025 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 1–6. IEEE, 2025. https://arxiv.org/pdf/2411.16806

ReSCIM: Variation-Resilient High Weight-Loading Bandwidth In-Memory Computation Based on Fine-Grained Hybrid Integration of Multi-Level ReRAM and SRAM Cells

Published in ICCAD, 2024

This paper presents a high weight-loading bandwidth hybrid ReRAM and SRAM cell for Computing-in-Memory.

Recommended citation: Xiaomeng Wang*, Jingyu He*, Kunming Shao, et al. ReSCIM: Variation-Resilient High Weight-Loading Bandwidth In-Memory Computation Based on Fine-Grained Hybrid Integration of Multi-Level ReRAM and SRAM Cells. In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pages 1–9. IEEE, 2024. https://doi.org/10.1145/3676536.3676751