419 citations · 771 across the 42 of their papers we have counts for
42 papers
BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models
Honghua Zhu, Chunjie Luo, Jianfeng Zhan
Accurate power estimation is important for understanding and optimizing CPU power behavior, yet practical workflows often rely on simulation-derived information or post-silicon ana…
KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
Kuiye Ding, Fanda Fan, Zheya Wang +5
In the World Wide Web, reliable time series forecasts provide the forward-looking signals that drive resource planning, cache placement, and anomaly response, enabling platforms to…
DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework
Kuiye Ding, Fanda Fan, Yao Wang +6
Multivariate Time Series Forecasting plays a key role in many applications. Recent works have explored using Large Language Models for MTSF to take advantage of their reasoning abi…
Could Bibliometrics Reveal Top Science and Technology Achievements and Researchers? The Case for Evaluatology-based Science and Technology Evaluation
Guoxin Kang, Wanling Gao, Lei Wang +5
By utilizing statistical methods to analyze bibliographic data, bibliometrics faces inherent limitations in identifying the most significant science and technology achievements and…
Evaluatology: The Science and Engineering of Evaluation
Jianfeng Zhan, Lei Wang, Wanling Gao +10
Evaluation is a crucial aspect of human existence and plays a vital role in various fields. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on…
AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI
Fanda Fan, Chunjie Luo, Wanling Gao +1
The burgeoning field of Artificial Intelligence Generated Content (AIGC) is witnessing rapid advancements, particularly in video generation. This paper introduces AIGCBench, a pion…
DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos
Chunjie Luo, Fei Luo, Yusen Wang +2
Reconstructing a dynamic human with loose clothing is an important but difficult task. To address this challenge, we propose a method named DLCA-Recon to create human avatars from…
Hierarchical Masked 3D Diffusion Model for Video Outpainting
Fanda Fan, Chaoxu Guo, Litong Gong +5
Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should mai…
WPC: Whole-picture Workload Characterization
Lei Wang, Kaiyong Yang, Chenxi Wang +7
This article raises an important and challenging workload characterization issue: can we uncover each critical component across the stacks contributing what percentages to any spec…
DCNetBench: Scaleable Data Center Network Benchmarking
Ke Liu, Wanling Gao, Chunjie Luo +7
Data center networking is the central infrastructure of the modern information society. However, benchmarking them is very challenging as the real-world network traffic is difficul…
Quality at the Tail of Machine Learning Inference
Zhengxin Yang, Wanling Gao, Chunjie Luo +4
Machine learning inference should be subject to stringent inference time constraints while ensuring high inference quality, especially in safety-critical (e.g., autonomous driving)…
High fusion computers: The IoTs, edges, data centers, and humans-in-the-loop as a computer
Wanling Gao, Lei Wang, Mingyu Chen +13
Emerging and future applications rely heavily upon systems consisting of Internet of Things (IoT), edges, data centers, and humans-in-the-loop. Significantly different from warehou…
Shift-and-Balance Attention
Chunjie Luo, Jianfeng Zhan, Tianshu Hao +2
Attention is an effective mechanism to improve the deep model capability. Squeeze-and-Excite (SE) introduces a light-weight attention branch to enhance the network's representation…
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
Zihan Jiang, Wanling Gao, Fei Tang +6
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the tr…
FLBench: A Benchmark Suite for Federated Learning
Yuan Liang, Yange Guo, Yanxia Gong +3
Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices so-called an isolated data i…
HPC AI500: The Methodology, Tools, Roofline Performance Models, and Metrics for Benchmarking HPC AI Systems
Zihan Jiang, Lei Wang, Xingwang Xiong +6
The recent years witness a trend of applying large-scale distributed deep learning in both business and scientific computing areas, whose goal is to speed up the training time to a…
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
Chunjie Luo, Jianfeng Zhan, Lei Wang +1
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN). Different from Batch Normalization (BN), which normalizes the final summation…
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Chunjie Luo, Xiwen He, Jianfeng Zhan +3
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains. The application of deep learning on the mobile and embedded devic…
AIBench Scenario: Scenario-distilling AI Benchmarking
Wanling Gao, Fei Tang, Jianfeng Zhan +7
Modern real-world application scenarios like Internet services consist of a diversity of AI and non-AI modules with huge code sizes and long and complicated execution paths, which…
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
Fei Tang, Wanling Gao, Jianfeng Zhan +30
Earlier-stage evaluations of a new AI architecture/system need affordable benchmarks. Only using a few AI component benchmarks like MLPerfalone in the other stages may lead to misl…
Extended Batch Normalization
Chunjie Luo, Jianfeng Zhan, Lei Wang +1
Batch normalization (BN) has become a standard technique for training the modern deep networks. However, its effectiveness diminishes when the batch size becomes smaller, since the…
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
Wanling Gao, Fei Tang, Jianfeng Zhan +31
Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process…
AIBench: An Industry Standard Internet Service AI Benchmark Suite
Wanling Gao, Fei Tang, Lei Wang +22
Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many inn…
HPC AI500: A Benchmark Suite for HPC AI Systems
Zihan Jiang, Wanling Gao, Lei Wang +10
In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great chal…