Convolutional network without multiplication operation

In September 2018, Google Research team released paper with the title “No Multiplication? No floating point? No problem? Training networks for efficient inference” which we will refer to as NMNF. The main building blocks of convolutional neural networks are convolutional layers and the great majority of inference time is spent in them. NMNF paper targets devices like hearing aids, earbuds or wearables. Such devices are highly resource constrained, in terms of memory, power, and computation, and therefore benefit from a specialized implementation of convolutional layer introduced in the paper. Inference-time floating point operations are not only energy-hungry compared to integer operations but also computationally demanding. NMNF approach avoids floating point operations entirely and consequently, we can enjoy reduced model size as well.
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