63 citations · 112 across the 4 of their papers we have counts for
4 papers
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott +11
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce s…
FPGAs-as-a-Service Toolkit (FaaST)
Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris +13
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically a…
GPU coprocessors as a service for deep learning inference in high energy physics
Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas +13
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limi…
FPGA-accelerated machine learning inference as a service for particle physics computing
Javier Duarte, Philip Harris, Scott Hauck +20
New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large pote…