18 citations · 29 across the 12 of their papers we have counts for
12 papers
Ekka: Automated Diagnosis of Silent Errors in LLM Inference
Yile Gu, Zhen Zhang, Shaowei Zhu +4
LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations. The rapid development process can introduce silent errors where output…
DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling
Yi Pan, Yile Gu, Jinbin Luo +7
Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different resource usage. However, its wid…
AgentFlux: Decoupled Fine-Tuning & Inference for On-Device Agentic Systems
Rohan Kadekodi, Zhan Jin, Keisuke Kamahori +5
The deployment of Large Language Models (LLMs) as agentic orchestrators has revolutionized task automation, but the need for privacy-preserving, cost-effective solutions demands on…
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
Yile Gu, Rohan Kadekodi, Hoang Nguyen +3
The recent shift in Generative AI (GenAI) applications from cloud-only environments to end-user devices introduces new challenges in resource management, system efficiency, and use…
Semantic Scheduling for LLM Inference
Wenyue Hua, Dujian Ding, Yile Gu +4
Conventional operating system scheduling algorithms are largely content-ignorant, making decisions based on factors such as latency or fairness without considering the actual inten…
Scalable and Accurate Application-Level Crash-Consistency Testing via Representative Testing
Yile Gu, Ian Neal, Jiexiao Xu +7
Crash consistency is essential for applications that must persist data. Crash-consistency testing has been commonly applied to find crash-consistency bugs in applications. The cras…
Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
Kan Zhu, Tian Tang, Qinyu Xu +7
Long-context models are essential for many applications but face inefficiencies in loading large KV caches during decoding. Prior methods enforce fixed token budgets for sparse att…
TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval
Chien-Yu Lin, Keisuke Kamahori, Yiyu Liu +11
Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage. Modern RAG pipelines rely o…
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models
Yile Gu, Yifan Xiong, Jonathan Mace +4
Observability in cloud infrastructure is critical for service providers, driving the widespread adoption of anomaly detection systems for monitoring metrics. However, existing syst…
NanoFlow: Towards Optimal Large Language Model Serving Throughput
Kan Zhu, Yufei Gao, Yilong Zhao +13
Large Language Models (LLMs) have resulted in a surging demand for planet-scale serving systems, where tens of thousands of GPUs continuously serve hundreds of millions of users. C…
Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models
Keisuke Kamahori, Tian Tang, Yile Gu +2
Large Language Models (LLMs) with the Mixture-of-Experts (MoE) architectures have shown promising performance on various tasks. However, due to the huge model sizes, running them i…
Reducing Energy Bloat in Large Model Training
Jae-Won Chung, Yile Gu, Insu Jang +3
Training large AI models on numerous GPUs consumes a massive amount of energy, making power delivery one of the largest limiting factors in building and operating datacenters for A…