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AutoML for Multilayer Perceptron and FPGA Co-design
In this work, we develop and test a general multilayer perceptron (MLP) flow that can take arbitrary datasets as input and automatically produce optimized NNAs and hardware designs.
Evolutionary Cell Aided Design for Neural Network Architectures
Mathematical theory shows us that multilayer feedforward Artificial Neural Networks(ANNs) are universal function approximators, capable of approximating any measurable function to any desired degree of accuracy.
Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs
This results in a trade-off between throughput and accuracy and can be tailored for different networks through various combinations of activation and weight data widths.
Distributed, Parallel, and Cluster Computing Hardware Architecture
