Convolutional neural network (CNN) offers significant accuracy in image
detection. To implement image detection using CNN in the internet of things
(IoT) devices, a streaming hardware accelerator is proposed. The proposed
accelerator optimizes the energy efficiency by avoiding unnecessary data
movement. With unique filter decomposition technique, the accelerator can
support arbitrary convolution window size. In addition, max pooling function
can be computed in parallel with convolution by using separate pooling unit,
thus achieving throughput improvement. A prototype accelerator was implemented
in TSMC 65nm technology with a core size of 5mm2. The accelerator can support
major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency
at 350mW, making it a promising hardware accelerator for intelligent IoT
devices.

该研究提出了一种流式硬件加速器，通过避免不必要的数据移动和独特的滤波器分解技术，优化了能源效率，并支持任意卷积窗口大小和使用并行池化单元计算最大池化功能，进而实现了吞吐量的提高和较高的能效比，可在智能物联网设备中应用。