6 papers
Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators
Haishan Zhu, Domi Yan, Michael Levesque-Dion +40
The rapid growth in machine learning workloads has fueled the proliferation of custom accelerator architectures. Designed from the ground up, these accelerators often expose progra…
Benchmarking Compound AI Applications for Hardware-Software Co-Design
Paramuth Samuthrsindh, Angel Cervantes, Varun Gohil +3
Compound AI applications, composed from interactions between Large Language Models (LLMs), Machine Learning (ML) models, external tools and data sources are quickly becoming an int…
Taming Serverless Cold Starts Through OS Co-Design
Ben Holmes, Baltasar Dinis, Lana Honcharuk +2
Serverless computing promises fine-grained elasticity and operational simplicity, fueling widespread interest from both industry and academia. Yet this promise is undercut by the c…
Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms
Gohar Irfan Chaudhry, Esha Choukse, Haoran Qiu +4
Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving th…
Checkmate: Zero-Overhead Model Checkpointing via Network Gradient Replication
Ankit Bhardwaj, Weiyang Wang, Jeremy Carin +2
This paper presents Checkmate, a system that enables per-iteration checkpointing in DNN training without any training slowdown. The traditional approach to checkpointing requires a…
Towards Resource-Efficient Compound AI Systems
Gohar Irfan Chaudhry, Esha Choukse, Ãñigo Goiri +3
Compound AI Systems, integrating multiple interacting components like models, retrievers, and external tools, have emerged as essential for addressing complex AI tasks. However, cu…