maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Abstract

This paper describes maxDNN, a computationally efficient convolution kernel for deep learning with the NVIDIA Maxwell GPU. maxDNN reaches 96.3% computational efficiency on typical deep learning network architectures. The design combines ideas from cuda-convnet2 with the Maxas SGEMM assembly code. We only address forward propagation (FPROP) operation of the network, but we believe that the same techniques used here will be effective for backward propagation (BPROP) as well.

7 pages, 2 figures, 1 table

Cited by in corpus (7)

• Pruning Convolutional Neural Networks for Resource Efficient Inference
• Origami: A 803 GOp/s/W Convolutional Network Accelerator
• Fast Algorithms for Convolutional Neural Networks
• A Systematic Approach to Blocking Convolutional Neural Networks
• Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
• A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications
• Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs