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Li Jiang (蒋力) — Shanghai Jiao Tong University
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2026-08-21
🎉 恭喜Jialin Zhan：《DELTA: Decoupling Latent Heterogeneity in Asymmetric Low-Rank Compression》 (被接收)，发表于 EMNLP 2026 CCF-B
🎉 Jialin Zhan: DELTA: Decoupling Latent Heterogeneity in Asymmetric Low-Rank Compression — accepted by EMNLP 2026 CCF-B
2026-08-21
🎉 恭喜甘梓言：《PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition》 (被接收)，发表于 EMNLP 2026 CCF-B
🎉 Ziyan Gan: PCoMoE: Shifting MoE Inference from Monolithic Expert Selection to Fine-Grained Path Composition — accepted by EMNLP 2026 CCF-B
2026-08-21
🎉 恭喜Yiwei Hu：《Seer: Efficient KV Cache Management for LLM Acceleration via Mitigating Semantic Search Inefficiency》 (被接收)，发表于 EMNLP 2026 CCF-B
🎉 Yiwei Hu: Seer: Efficient KV Cache Management for LLM Acceleration via Mitigating Semantic Search Inefficiency — accepted by EMNLP 2026 CCF-B
2026-08-20
🎉 恭喜Xuwen Zhou：《Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference》 (被接收)，发表于 ACL 2026 CCF-A
🎉 Xuwen Zhou: Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference — accepted by ACL 2026 CCF-A
2026-08-20
🎉 恭喜Ning Yang：《NICE: Deep Neural Network Acceleration via Hardware-Friendly Index Assisted Compression》 (被接收)，发表于 ACM TACO 2026 CCF-A
🎉 Ning Yang: NICE: Deep Neural Network Acceleration via Hardware-Friendly Index Assisted Compression — accepted by ACM TACO 2026 CCF-A
2026-08-20
🎉 恭喜李皓民：《GEMM-GS: Accelerating 3D Gaussian Splatting on Tensor Cores with GEMM-Compatible Blending》 (被接收)，发表于 DAC 2026 CCF-A
🎉 Haomin Li: GEMM-GS: Accelerating 3D Gaussian Splatting on Tensor Cores with GEMM-Compatible Blending — accepted by DAC 2026 CCF-A
2026-08-20
🎉 恭喜Yilong Zhao：《PUSHtap: PIM-based In-Memory HTAP with Unified Data Storage Format》 (被接收)，发表于 ASPLOS 2026 CCF-A
🎉 Yilong Zhao: PUSHtap: PIM-based In-Memory HTAP with Unified Data Storage Format — accepted by ASPLOS 2026 CCF-A
2026-08-14
🎉 恭喜李皓民、刘方鑫（共一）：《Look Once, Compute Less: A Spatio-Temporal Redundancy-Aware CIM Accelerator for Adaptive BEV Representation》 (被接收)，发表于 IEEE TCAD 2026 CCF-A
🎉 Haomin Li, Fangxin Liu (co-first): Look Once, Compute Less: A Spatio-Temporal Redundancy-Aware CIM Accelerator for Adaptive BEV Representation — accepted by IEEE TCAD 2026 CCF-A
2026-08-14
🎉 恭喜刘方鑫、Xin Ju（共一）：《SqzAct: Taming Activation Outliers for Efficient 4-bit LLM Inference via Block-Level Squeezing》 (被接收)，发表于 IEEE TCAD 2026 CCF-A
🎉 Fangxin Liu, Xin Ju (co-first): SqzAct: Taming Activation Outliers for Efficient 4-bit LLM Inference via Block-Level Squeezing — accepted by IEEE TCAD 2026 CCF-A
2026-07-08
🎉 恭喜刘方鑫：《MOSAIC: Exploiting Structured Tolerance for Adaptive LLM Inference Mapping in Heterogeneous PIM Accelerators》 (被接收)，发表于 MICRO 2026 CCF-A
🎉 Fangxin Liu: MOSAIC: Exploiting Structured Tolerance for Adaptive LLM Inference Mapping in Heterogeneous PIM Accelerators — accepted by MICRO 2026 CCF-A
2026-07-08
🎉 恭喜汪宗武：《ULTRA: Bridging the VQ Gap in Anisotropic LLM Quantization via a Unified LUT-Based Transformer Architecture》 (被接收)，发表于 MICRO 2026 CCF-A
🎉 Zongwu Wang: ULTRA: Bridging the VQ Gap in Anisotropic LLM Quantization via a Unified LUT-Based Transformer Architecture — accepted by MICRO 2026 CCF-A
2026-03-28
🎉 恭喜Yilong Zhao：《COMET: A Cooperative Scheduling Framework for Concurrent PIM/CPU Execution on Mobile Devices》 (被接收)，发表于 ISCA 2026 CCF-A — Best Paper Award Finalist（五篇候选之一）
🎉 Yilong Zhao: COMET: A Cooperative Scheduling Framework for Concurrent PIM/CPU Execution on Mobile Devices — accepted by ISCA 2026 CCF-A — Best Paper Award Finalist (one of five finalists)
2026-03-28
🎉 恭喜Jingkui Yang：《Harmonia: A Unified Hierarchical Scheduling Framework for Sparse Matrix Multiplication》 (被接收)，发表于 ISCA 2026 CCF-A
🎉 Jingkui Yang: Harmonia: A Unified Hierarchical Scheduling Framework for Sparse Matrix Multiplication — accepted by ISCA 2026 CCF-A
2026-03-28
🎉 恭喜刘方鑫：《STEP: Adaptive Spatio-Temporal Expert Prefetching for Low-Latency and Memory-Efficient MoE Inference》 (被接收)，发表于 ISCA 2026 CCF-A
🎉 Fangxin Liu: STEP: Adaptive Spatio-Temporal Expert Prefetching for Low-Latency and Memory-Efficient MoE Inference — accepted by ISCA 2026 CCF-A