Condensation-Net: Memory-Efficient Network Architecture With Cross-Channel Pooling Layers and Virtual Feature Maps

Abstract

Lightweight convolutional neural networks" is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware platform, it is necessary to reduce the memory size and the computational cost. The contribution of this paper is stated as follows. First, we propose an algorithm to process a specific network architecture (Condensation-Net) without increasing the maximum memory storage for feature maps. The architecture for virtual feature maps saves 26.5% of memory bandwidth by calculating the results of cross-channel pooling before storing the feature map into the memory. Second, we show that cross-channel pooling can improve the accuracy of object detection tasks, such as face detection, because it increases the number of filter weights. Compared with Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and 1.5% for full-precision networks when the false-positive rate is 0.1. Last but not the least, the analysis results show that the overhead to support the cross-channel pooling with the proposed hardware architecture is negligible small. The extra memory cost to support Condensation-Net is 0.2% of the total size, and the extra gate count is only 1.0% of the total size.

Hardware Architecture of Embedded Inference Accelerator and Analysis of Algorithms for Depthwise and Large-Kernel Convolutions

“…Network Top-1 Accuracy Top-5 Accuracy MobileNetV1-0.25 [5] 68.39% 88.35% MobileNetV1-0.25 with 5 × 5 filters 69.44% 89.90% MobileNetV1-0.25 with dilated filters (A) 1 68.52% 88.61% MobileNetV1-0.25 with dilated filters (B) 2 68.98% 88.85%…”

“…Deep learning has been widely applied to image processing and computer vision applications. In the field of embedded vision and robotics, it is important to implement convolutional neural networks (CNNs) with low computational costs [1]. Many researchers propose efficient algorithms to accelerate the deep-learningbased algorithms while keeping the recognition accuracy [2,4,5,8,10,12,13].…”