7 citations · 11 across the 5 of their papers we have counts for
5 papers
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…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
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…
Tailor: Altering Skip Connections for Resource-Efficient Inference
Olivia Weng, Gabriel Marcano, Vladimir Loncar +7
Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and of…
FastStamp: Accelerating Neural Steganography and Digital Watermarking of Images on FPGAs
Shehzeen Hussain, Nojan Sheybani, Paarth Neekhara +3
Steganography and digital watermarking are the tasks of hiding recoverable data in image pixels. Deep neural network (DNN) based image steganography and watermarking techniques are…