4 citations · 7 across the 6 of their papers we have counts for
7 papers
KForge: LLM-Driven Cross-Platform Kernel Generation for AI Accelerators
Taras Sereda, Burak Bartan, Ankita Nayak +3
Production inference increasingly targets a heterogeneous mix of accelerators. Agentic pipelines interleave reasoning, tool calls, and multi-agent coordination, each with distinct…
KForge: Program Synthesis for Diverse AI Hardware Accelerators
Taras Sereda, Tom St. John, Burak Bartan +3
GPU kernels are critical for ML performance but difficult to optimize across diverse accelerators. We present KForge, a platform-agnostic framework built on two collaborative LLM-b…
Efficient and Scalable Agentic AI with Heterogeneous Systems
Zain Asgar, Michelle Nguyen, Sachin Katti
AI agents are emerging as a dominant workload in a wide range of applications, promising to be the vehicle that delivers the promised benefits of AI to enterprises and consumers. U…
Machine Learning Sensors
Pete Warden, Matthew Stewart, Brian Plancher +6
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from c…
Bringing Source-Level Debugging Frameworks to Hardware Generators
Keyi Zhang, Zain Asgar, Mark Horowitz
High-level hardware generators have significantly increased the productivity of design engineers. They use software engineering constructs to reduce the repetition required to expr…
ML-EXray: Visibility into ML Deployment on the Edge
Hang Qiu, Ioanna Vavelidou, Jian Li +5
Benefiting from expanding cloud infrastructure, deep neural networks (DNNs) today have increasingly high performance when trained in the cloud. Researchers spend months of effort c…
Characterizing and Taming Model Instability Across Edge Devices
Eyal Cidon, Evgenya Pergament, Zain Asgar +2
The same machine learning model running on different edge devices may produce highly-divergent outputs on a nearly-identical input. Possible reasons for the divergence include diff…