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Found 4 papers, 1 papers with code
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models.
End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
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).
Reliable edge machine learning hardware for scientific applications
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing.
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
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-specific integrated circuit (ASIC) hardware.
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