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cs.DS2021
dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference
Elias Trommer, Bernd Waschneck, Akash Kumar
Reducing the memory footprint of neural networks is a crucial prerequisite for deploying them in small and low-cost embedded devices. Network parameters can often be reduced signif…
cs.PL2021
Compiler Toolchains for Deep Learning Workloads on Embedded Platforms
Max Sponner, Bernd Waschneck, Akash Kumar
As the usage of deep learning becomes increasingly popular in mobile and embedded solutions, it is necessary to convert the framework-specific network representations into executab…