Computer vision performances have been significantly improved in recent years
by Convolutional Neural Networks(CNN). Currently, applications using CNN
algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs
or FPGAs. However, power consumption, speed, accuracy, memory footprint, and
die size should all be taken into consideration for mobile and embedded
applications. Domain Specific Architecture (DSA) for CNN is the efficient and
practical solution for CNN deployment and implementation. We designed and
produced a 28nm Two-Dimensional CNN-DSA accelerator with an ultra
power-efficient performance of 9.3TOPS/Watt and with all processing done in the
internal memory instead of outside DRAM. It classifies 224x224 RGB image inputs
at more than 140fps with peak power consumption at less than 300mW and an
accuracy comparable to the VGG benchmark. The CNN-DSA accelerator is
reconfigurable to support CNN model coefficients of various layer sizes and
layer types, including convolution, depth-wise convolution, short-cut
connections, max pooling, and ReLU. Furthermore, in order to better support
real-world deployment for various application scenarios, especially with
low-end mobile and embedded platforms and MCUs (Microcontroller Units), we also
designed algorithms to fully utilize the CNN-DSA accelerator efficiently by
reducing the dependency on external accelerator computation resources,
including implementation of Fully-Connected (FC) layers within the accelerator
and compression of extracted features from the CNN-DSA accelerator. Live demos
with our CNN-DSA accelerator on mobile and embedded systems show its
capabilities to be widely and practically applied in the real world.

该论文介绍了一种用于实现基于卷积神经网络的计算机视觉应用的高效领域特定架构（DSA），并利用 DSA 设计了一个能够高效地支持移动设备和嵌入式系统的加速器，可用于实时图像分类和场景识别等实际应用。