Architectures and algorithms for user customization of CNNs

In this paper we present a convolutional neural network architecture that supports user customization through incremental transfer learning. The architecture consists of a large basic inference engine and a small augmenting engine. After training the basic inference engine and augmenting engine on a large general dataset, the basic inference engine is fixed. For user customization, only the augmenting engine is re-trained on-device using a small user specific dataset provided by the user. To accelerate the training of the augmenting engine we map this to a coarsegrained reconfigurable array processor. The complete network architecture is evaluated using the Caffe framework, and a C-code equivalent network is implemented and tested on a CGRA processor. Experiments with NIST'19 and our user-specific datasets show an increase in accuracy of the system from 76.3% to 93.2% after user customization. Mapping this code to a CGRA gives us a speed up of 45x and a 49-and 3-fold reduced energy consumption over an ARMv7 processor and a 3-way VLIW processor, respectively, showing the potential of CGRAs as DNN processors.