7 citations · 11 across the 5 of their papers we have counts for
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cs.LG2024
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…
cs.LG2023★ 1 cited
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…