Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

by   Wei Niu, et al.

Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped with powerful, high-end mobile CPUs and GPUs. However, it is still a challenging task to execute 3D Convolutional Neural Networks (CNNs) targeting for real-time performance, besides high inference accuracy. The reason is more complex model structure and higher model dimensionality overwhelm the available computation/storage resources on mobile devices. A natural way may be turning to deep learning weight pruning techniques. However, the direct generalization of existing 2D CNN weight pruning methods to 3D CNNs is not ideal for fully exploiting mobile parallelism while achieving high inference accuracy. This paper proposes RT3D, a model compression and mobile acceleration framework for 3D CNNs, seamlessly integrating neural network weight pruning and compiler code generation techniques. We propose and investigate two structured sparsity schemes i.e., the vanilla structured sparsity and kernel group structured (KGS) sparsity that are mobile acceleration friendly. The vanilla sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained structured sparsity that enjoys higher flexibility while exploiting full on-device parallelism. We propose a reweighted regularization pruning algorithm to achieve the proposed sparsity schemes. The inference time speedup due to sparsity is approaching the pruning rate of the whole model FLOPs (floating point operations). RT3D demonstrates up to 29.1× speedup in end-to-end inference time comparing with current mobile frameworks supporting 3D CNNs, with moderate 1 frames could be within 150 ms, when executing representative C3D and R(2+1)D models on a cellphone. For the first time, real-time execution of 3D CNNs is achieved on off-the-shelf mobiles.


PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

With the emergence of a spectrum of high-end mobile devices, many applic...

Algorithm to Compilation Co-design: An Integrated View of Neural Network Sparsity

Reducing computation cost, inference latency, and memory footprint of ne...

FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks

There exists a plethora of techniques for inducing structured sparsity i...

Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

The success of convolutional neural networks (CNNs) in computer vision a...

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

The growing energy and performance costs of deep learning have driven th...

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Weight pruning has been widely acknowledged as a straightforward and eff...

26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone

With the rapid emergence of a spectrum of high-end mobile devices, many ...

Please sign up or login with your details

Forgot password? Click here to reset