4 papers
Ascend-RaBitQ: Heterogeneous NPU-CPU Acceleration of Billion-Scale Similarity Search with 1-bit Quantization
Fujun He, Chuyue Ye, Huaxiang Cai +12
Vector similarity search is a critical component of modern AI systems, but traditional CPU-based implementations face fundamental scalability bottlenecks for billion-scale corpora…
Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Yan Li, Zhenyu Zhang, Zhengang Wang +2
Prevailing LLM serving engines employ expert parallelism (EP) to implement multi-device inference of massive MoE models. However, the efficiency of expert parallel inference is lar…
Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions
Yuanhao Lai, Pengfei Zheng, Chenpeng Ji +5
Gaussian-Process-based Bayesian optimization (GP-BO), is a prevailing model-based framework for DBMS auto-tuning. However, recent work shows GP-BO-based DBMS auto-tuners significan…
CLEANet: Robust and Efficient Anomaly Detection in Contaminated Multivariate Time Series
Songhan Zhang, Yuanhao Lai, Pengfei Zheng +4
Multivariate time series (MTS) anomaly detection is essential for maintaining the reliability of industrial systems, yet real-world deployment is hindered by two critical challenge…