1 citations · 1 across the 5 of their papers we have counts for
5 papers
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
Tommaso Baldi, Javier Campos, Olivia Weng +4
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensin…
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
Giuseppe Di Guglielmo, Botao Du, Javier Campos +10
We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing…
Reliable edge machine learning hardware for scientific applications
Tommaso Baldi, Javier Campos, Ben Hawks +15
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementatio…
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
Javier Campos, Zhen Dong, Javier Duarte +4
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specif…