82 citations · 273 across the 15 of their papers we have counts for
15 papers
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation
Yaoyao Ding, Bohan Hou, Xiao Zhang +5
Serving Large Language Models (LLMs) is critical for AI-powered applications, yet it demands substantial computational resources, particularly in memory bandwidth and computational…
Automated Deep Learning Optimization via DSL-Based Source Code Transformation
Ruixin Wang, Minghai Lu, Cody Hao Yu +2
As deep learning models become increasingly bigger and more complex, it is critical to improve model training and inference efficiency. Though a variety of highly optimized librari…
SGLang: Efficient Execution of Structured Language Model Programs
Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie +9
Large language models (LLMs) are increasingly used for complex tasks that require multiple generation calls, advanced prompting techniques, control flow, and structured inputs/outp…
Efficient Memory Management for Large Language Model Serving with PagedAttention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang +6
High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV c…
RAF: Holistic Compilation for Deep Learning Model Training
Cody Hao Yu, Haozheng Fan, Guangtai Huang +9
As deep learning is pervasive in modern applications, many deep learning frameworks are presented for deep learning practitioners to develop and train DNN models rapidly. Meanwhile…
Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training
Hongzheng Chen, Cody Hao Yu, Shuai Zheng +3
Recent years have seen an increase in the development of large deep learning (DL) models, which makes training efficiency crucial. Common practice is struggling with the trade-off…
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
Yaoyao Ding, Cody Hao Yu, Bojian Zheng +3
As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide effic…
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Siyuan Feng, Bohan Hou, Hongyi Jin +8
Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimen…
Tensor Program Optimization with Probabilistic Programs
Junru Shao, Xiyou Zhou, Siyuan Feng +7
Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search spa…
Bring Your Own Codegen to Deep Learning Compiler
Zhi Chen, Cody Hao Yu, Trevor Morris +6
Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of the…
AutoDSE: Enabling Software Programmers to Design Efficient FPGA Accelerators
Atefeh Sohrabizadeh, Cody Hao Yu, Min Gao +1
Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for softw…
Ansor: Generating High-Performance Tensor Programs for Deep Learning
Lianmin Zheng, Chengfan Jia, Minmin Sun +9
High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However, obtaining performant tensor programs for different operators on vari…
AutoAccel: Automated Accelerator Generation and Optimization with Composable, Parallel and Pipeline Architecture
Jason Cong, Peng Wei, Cody Hao Yu +1
CPU-FPGA heterogeneous architectures are attracting ever-increasing attention in an attempt to advance computational capabilities and energy efficiency in today's datacenters. Thes…
Best-Effort FPGA Programming: A Few Steps Can Go a Long Way
Jason Cong, Zhenman Fang, Yuchen Hao +4
FPGA-based heterogeneous architectures provide programmers with the ability to customize their hardware accelerators for flexible acceleration of many workloads. Nonetheless, such…