Author

Yong Dou

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#large language models Book Open access Sep 2026

TileGEMM: Boosting the Performance of GEMM on AMX-Powered CPUs by Exploiting Data Reuse

General Matrix Multiplication (GEMM) is the cornerstone of high-performance computing and deep learning. Its efficiency significantly influences the performance of applications ranging from large language models to scientific simulations. Intel Advanced Matrix Extensions (AMX) significantly boost matrix operations throughput, yet existing implementations underutilize its potential. The core bottleneck is that AMX’s unique microarchitectural constraints break the classic Advanced Vector Extensions 512 (AVX-512) GEMM paradigm, causing critical data reuse loss across the memory hierarchy: inefficient vector register reuse during mandatory layout transformation, poorly reconciled trade-off between Tile register reuse for partial sums and L1 cache locality, and severe buffer reuse degradation due to fine-grained update strategies. To address these challenges, we propose TileGEMM, a high-performance GEMM implementation on AMX that systematically enhances data reuse across the memory hierarchy. First, a dual Tile register layout transformation strategy eliminates redundant memory accesses, maximizing vector register reuse. Second, a multi-level cache- and Tile register-aware tiling strategy adopts a K-dimension-centric model under strict L1 capacity constraints, improving Tile register reuse for intermediate partial sums while preserving L1 cache locality. Third, a coarse-grained buffer update strategy with loop reordering extends transformed data residency, boosting buffer reuse and reducing transformation overhead. Extensive experiments in various matrix configurations show that TileGEMM achieves average speedups of 3.27 × and 1.27 × over AMX-based implementations LIBXSMM and OneDNN, and 10.30 × and 7.96 × over AVX-512-based implementations TVM and MKL, respectively.

Kang-Kang Chen, Hua-You Su, Meng-Han Jia et al. · 0 citations

2026

Audio Active Learning With Noisy Labels

Audio annotation is particularly costly and prone to errors due to the temporal nature and semantic ambiguity in audio perception. Active learning (AL) addresses this by iteratively selecting the most informative samples from an unlabeled pool for expert labeling, thereby maximizing model performance with minimal annotation effort. In the standard continual fine-tuning framework widely adopted in audio AL, the model trained in each cycle serves as the initialization for the next, preserving accumulated knowledge to enhance both sample selection and model accuracy. However, we discover a critical limitation when annotation noise is inevitably introduced: this default continual fine-tuning approach becomes susceptible to Primacy Bias — a phenomenon where early-learned patterns persistently influence subsequent learning. Our experiments show that this bias causes the model to overfit noise more rapidly in later cycles. To our knowledge, this represents the first comprehensive study specifically addressing Active Learning with Noisy Labels (ALNL) in the audio domain. To address this issue, we introduce a re-initialization strategy for ALNL scenarios. Our experiments demonstrate that periodically resetting model parameters preserves the model’s ability to learn from clean samples. Furthermore, we propose the Self-Purify Active Learning (SPAL) method, which dynamically identifies potential label noise via training loss modeling and supports either human-in-the-loop correction or automated label refurbishment. Extensive experiments on underwater acoustics, general audio, and speech datasets demonstrate the effectiveness of our framework against label noise in AL scenarios.

Yi Su, Hui Geng, Qisheng Xu et al. · IEEE Transactions on Audio,... · 0 citations