Neural network accelerator with fast buffer design for computer vision

Recently, the neural networks with convolution computation is widely used for image classification and recognition. For real-time implementation, the video buffer is required to store the image temperately. However, traditional buffers like CLSB (content line shift buffer) may experience delays during the read process, particularly when encountering line breaks or image changes. As for N × N convolution, the delay time is N−1 clocks for every row changing. As the image width is W, the delay time is 2W + N clocks for every frame changing. These delays can impact the efficiency and performance of the neural network. To overcome this challenge, this paper presented novel buffer design to avoid the delay at the line ends and frame change. By proactively fetching data ahead of time, the buffer can dynamically schedule the read operation and ensure that the subsequent data are correctly placed for efficient processing. This improvement in read latency contributes to enhanced performance and better utilization of computational resources within the hardware system. Then the full convolutional network accelerator is implemented with the fast buffer design and common computational kernel to save the hardware cost based on LeNet model. The results show that the accuracy can achieve 99.1% with MNIST dataset verification. By eliminating the waiting time, the modified buffer allows for more efficient processing in the image, and the fame rate for a computer vision can achieve 46 per second, to meet the real-time requirement.