20 citations · 35 across the 12 of their papers we have counts for
13 papers
Benchmarking LLMs on File System Design and Implementation
Yuqi Xue, Daixuan Li, Jian Huang
Large Language Models (LLMs) are fundamentally transforming computer system research and development. As we employ LLMs in file system (fs) development, it is essential to understa…
Auto-Scaling Heterogeneous Neural Processing Units for Energy and Cost-Efficient LLM Serving
Yuqi Xue, Jichuan Chang, Jian Huang
To meet the ever-increasing computing demands of large language model (LLM) services, modern cloud platforms have widely deployed neural processing units (NPUs). These NPU chips ha…
Enabling Spatially Fine-Grained DVFS in Neural Processing Units for Energy-Efficient LLM Serving
Yuqi Xue, Jerry Wu, Corey Yu +1
As neural processing units (NPUs) evolve rapidly to accommodate the ever-increasing compute demand of large language models (LLMs), their power consumption is becoming a limiting f…
Vegas: Self-Speculative Decoding with Verification-Guided Sparse Attention
Yikang Yue, Yuqi Xue, Jian Huang
Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the increasing memory demands of its KV c…
Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs
Xingang Guo, Yaxin Li, Xiangyi Kong +62
Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. Ho…
ReGate: Enabling Power Gating in Neural Processing Units
Yuqi Xue, Jian Huang
The energy efficiency of neural processing units (NPU) is playing a critical role in developing sustainable data centers. Our study with different generations of NPU chips reveals…
ELK: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques
Yiqi Liu, Yuqi Xue, Noelle Crawford +2
To meet the increasing demand of deep learning (DL) models, AI chips are employing both off-chip memory (e.g., HBM) and high-bandwidth low-latency interconnect for direct inter-cor…
SkyByte: Architecting an Efficient Memory-Semantic CXL-based SSD with OS and Hardware Co-design
Haoyang Zhang, Yuqi Xue, Yirui Eric Zhou +2
The CXL-based solid-state drive (CXL-SSD) provides a promising approach towards scaling the main memory capacity at low cost. However, the CXL-SSD faces performance challenges due…
Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T10
Yiqi Liu, Yuqi Xue, Yu Cheng +4
As AI chips incorporate numerous parallelized cores to scale deep learning (DL) computing, inter-core communication is enabled recently by employing high-bandwidth and low-latency…
Hardware-Assisted Virtualization of Neural Processing Units for Cloud Platforms
Yuqi Xue, Yiqi Liu, Lifeng Nai +1
Cloud platforms today have been deploying hardware accelerators like neural processing units (NPUs) for powering machine learning (ML) inference services. To maximize the resource…
G10: Enabling An Efficient Unified GPU Memory and Storage Architecture with Smart Tensor Migrations
Haoyang Zhang, Yirui Eric Zhou, Yuqi Xue +2
To break the GPU memory wall for scaling deep learning workloads, a variety of architecture and system techniques have been proposed recently. Their typical approaches include memo…
RackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design
Benjamin Reidys, Yuqi Xue, Daixuan Li +5
Software-defined networking (SDN) and software-defined flash (SDF) have been serving as the backbone of modern data centers. They are managed separately to handle I/O requests. At…
Building A Trusted Execution Environment for In-Storage Computing
Yuqi Xue, Luyi Kang, Weiwei Jia +7
In-storage computing with modern solid-state drives (SSDs) enables developers to offload programs from the host to the SSD. It has been proven to be an effective approach to allevi…