collaborators

6 papers

cs.AR2026

31.1 A 14.08-to-135.69Token/s ReRAM-on-Logic Stacked Outlier-Free Large-Language-Model Accelerator with Block-Clustered Weight-Compression and Adaptive Parallel-Speculative-Decoding

Pingcheng Dong, Yonghao Tan, Xuejiao Liu +13

This work presents a 55nm speculative decoding-based LLM accelerator with bumping-based face-to-face ReRAM-on-logic stacking technology. It features a local rotation unit for outli…

cs.AR2026

Expert Streaming: Accelerating Low-Batch MoE Inference via Multi-chiplet Architecture and Dynamic Expert Trajectory Scheduling

Songchen Ma, Hongyi Li, Weihao Zhang +8

Mixture-of-Experts is a promising approach for edge AI with low-batch inference. Yet, on-device deployments often face limited on-chip memory and severe workload imbalance; the pre…

cs.CR2026

Towards Secure and Efficient DNN Accelerators via Hardware-Software Co-Design

Wei Xuan, Zihao Xuan, Rongliang Fu +8

The rapid deployment of deep neural network (DNN) accelerators in safety-critical domains such as autonomous vehicles, healthcare systems, and financial infrastructure necessitates…

eess.IV2026

A 28nm 0.22μJ/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation

Pingcheng Dong, Yonghao Tan, Xuejiao Liu +14

This work presents a 28nm 13.93mm2 CNN-Transformer accelerator for semantic segmentation, achieving 3.86-to-10.91x energy reduction over previous designs. It features a hybrid atte…

cs.AR2025

SeDA: Secure and Efficient DNN Accelerators with Hardware/Software Synergy

Wei Xuan, Zhongrui Wang, Lang Feng +6

Ensuring the confidentiality and integrity of DNN accelerators is paramount across various scenarios spanning autonomous driving, healthcare, and finance. However, current security…

cs.AR2025

APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-Design

Yonghao Tan, Pingcheng Dong, Yongkun Wu +8

DNN accelerators, significantly advanced by model compression and specialized dataflow techniques, have marked considerable progress. However, the frequent access of high-precision…