activity
20202024
most citedUnderstanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

76 citations · 119 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AR20242 cited

RapidStream IR: Infrastructure for FPGA High-Level Physical Synthesis

Jason Lau, Yuanlong Xiao, Yutong Xie +7

The increasing complexity of large-scale FPGA accelerators poses significant challenges in achieving high performance while maintaining design productivity. High-level synthesis (H…

cs.PL202440 cited

Allo: A Programming Model for Composable Accelerator Design

Hongzheng Chen, Niansong Zhang, Shaojie Xiang +3

Special-purpose hardware accelerators are increasingly pivotal for sustaining performance improvements in emerging applications, especially as the benefits of technology scaling co…

cs.LG202376 cited

Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

Hongzheng Chen, Jiahao Zhang, Yixiao Du +5

Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majorit…

cs.AR2021

Dagger: Accelerating RPCs in Cloud Microservices Through Tightly-Coupled Reconfigurable NICs

Nikita Lazarev, Shaojie Xiang, Neil Adit +2

The ongoing shift of cloud services from monolithic designs to microservices creates high demand for efficient and high performance datacenter networking stacks, optimized for fine…

cs.AR20201 cited

Dagger: Towards Efficient RPCs in Cloud Microservices with Near-Memory Reconfigurable NICs

Nikita Lazarev, Neil Adit, Shaojie Xiang +2

Cloud applications are increasingly relying on hundreds of loosely-coupled microservices to complete user requests that meet an applications end-to-end QoS requirements. Communicat…