FPTalks 2024 was held online on July 11, 2024 on Zoom. Talks were 10 minutes long, followed by audience questions over Slack. Talks were recorded and are available on Youtube!
Welcome (Recording)
Session 1
8-bit Transformer Inference and Fine-tuning for Edge Accelerators
Jeffrey Yu, Stanford University
Precision Learning for DNN Compression via Adaptive Quantization
Cédric Gernigon, Inria Rennes
Accumulator-Aware Quantization with Guaranteed Overflow Avoidance
Ian Colbert, AMD Software Architecture
FTTN: Feature-Targeted Testing of NVIDIA & AMD Matrix Accelerators
Xinyi Li, Pacific Northwest National Laboratory
Break
Session 2
Type-based approaches to rounding error analysis
Ariel E. Kellison, Cornell University
End-to-End Verification of a Fast and Accurate Floating-Point Approximation
Guillaume Melquiond, Université Paris Saclay, Inria
Bit Blasting Probabilistic Programs
Guy Van den Broeck, University of California, Los Angeles
A Formal Specification of Tensor Cores via Satisfiability Modulo Theories
Benjamin Valpey, University of Rochester
Break
Session 3
Verification of Digital Numerics for High Consequence Systems
Sam Pollard, Sandia National Laboratory
Predicting Performance and Accuracy for Precision Tuning
Yutong Wang, University of California, Davis
An Overview of the NASA LaRC Tool Suite for Floating-Point Analysis
Mariano Moscato, NASA LaRC / AMA Inc.
Customizing Elementary Function Approximations for Hardware Accelerators
Benjamin Carleton, Cornell University
Conclusion
For more information, please see the FPBench project and check out past recordings from:
The FPTalks Workshop Series is supported in part by Department of Energy via the DOE X-Stack FP: Comport project.