19 citations · 36 across the 20 of their papers we have counts for
5 papers · 1 filter
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula +8
Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist,…
Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
Samuel Kushnir, Kimia Noorbakhsh, Kavya Sreedhar +6
Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models an…
JAXBench: Benchmarking Autonomous TPU Kernel Optimization
Arya Tschand, Charles Hong, Julian Walker +7
Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization by establishing a shared target to hillclimb on, but no equivalent exists for TPUs. We pr…
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…
Characterizing VLA Models: Identifying the Action Generation Bottleneck for Edge AI Architectures
Manoj Vishwanathan, Suvinay Subramanian, Anand Raghunathan
Vision-Language-Action (VLA) models are an emerging class of workloads critical for robotics and embodied AI at the edge. As these models scale, they demonstrate significant capabi…