Architectures and algorithms for user customization of CNNs
Barend Harris, Mansureh Shahraki Moghaddam, Duseok Kang, Inpyo Bae, Euiseok Kim, Hyemi Min, Hansu Cho, Sukjin Kim, Bernhard Egger, Soonhoi Ha, Ki‐Young Choi · 2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC) · 2018
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.