Paper Reading AI Learner

DSConv: Efficient Convolution Operator

2019-01-07 17:18:16
Marcelo Gennari, Roger Fawcett, Victor Adrian Prisacariu

Abstract

We introduce a variation of the convolutional layer called DSConv (Distribution Shifting Convolution) that can be readily substituted into standard neural network architectures and achieve both lower memory usage and higher computational speed. DSConv breaks down the traditional convolution kernel into two components: Variable Quantized Kernel (VQK), and Distribution Shifts. Lower memory usage and higher speeds are achieved by storing only integer values in the VQK, whilst preserving the same output as the original convolution by applying both kernel and channel based distribution shifts. We test DSConv in ImageNet on ResNet50 and 34, as well as AlexNet and MobileNet. We achieve a reduction in memory usage of up to 14x in the convolutional kernels and speed up operations of up to 10x by substituting floating point operations to integer operations. Furthermore, unlike other quantization approaches, our work allows for a degree of retraining to new tasks and datasets.

Abstract (translated)

URL

https://arxiv.org/abs/1901.01928

PDF

https://arxiv.org/pdf/1901.01928.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot