2-bit Model Compression of Deep Convolutional Neural Network on ASIC
  Engine for Image Retrieval
  Image retrieval utilizes image descriptors to retrieve the most similar
images to a given query image. Convolutional neural network (CNN) is becoming
the dominant approach to extract image descriptors for image retrieval. For
low-power hardware implementation of image retrieval, the drawback of CNN-based
feature descriptor is that it requires hundreds of megabytes of storage. To
address this problem, this paper applies deep model quantization and
compression to CNN in ASIC chip for image retrieval. It is demonstrated that
the CNN-based features descriptor can be extracted using as few as 2-bit
weights quantization to deliver a similar performance as floating-point model
for image retrieval. In addition, to implement CNN in ASIC, especially for
large scale images, the limited buffer size of chips should be considered. To
retrieve large scale images, we propose an improved pooling strategy, region
nested invariance pooling (RNIP), which uses cropped sub-images for CNN.
Testing results on chip show that integrating RNIP with the proposed 2-bit CNN
model compression approach is capable of retrieving large scale images.
