1 citations · 2 across the 4 of their papers we have counts for
4 papers
How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
Hanjiang Wu, Abhimanyu Rajeshkumar Bambhaniya, Sarbartha Banerjee +9
Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model sizes and tight TTFT and TPOT service-level objectives: from chunked-pre…
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Mark Mazumder +24
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) ev…
Characterizing FaaS Workflows on Public Clouds: The Good, the Bad and the Ugly
Varad Kulkarni, Nikhil Reddy, Tuhin Khare +3
Function-as-a-service (FaaS) is a popular serverless computing paradigm for developing event-driven functions that elastically scale on public clouds. FaaS workflows, such as AWS S…
Parallelizing Quantum-Classical Workloads: Profiling the Impact of Splitting Techniques
Tuhin Khare, Ritajit Majumdar, Rajiv Sangle +3
Quantum computers are the next evolution of computing hardware. Quantum devices are being exposed through the same familiar cloud platforms used for classical computers, and enabli…