SkipNet: Learning Dynamic Routing in Convolutional Networks

While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observation by learning to skip convolutional layers on a per-input basis. We introduce SkipNet, a modified residual network, that uses a gating network to selectively skip convolutional blocks based on the activations of the previous layer. We formulate the dynamic skipping problem in the context of sequential decision making and propose a hybrid learning algorithm that combines supervised learning and reinforcement learning to address the challenges of non-differentiable skipping decisions. We show SkipNet reduces computation by 30-90% while preserving the accuracy of the original model on four benchmark datasets and outperforms the state-of-the-art dynamic networks and static compression methods. We also qualitatively evaluate the gating policy to reveal a relationship between image scale and saliency and the number of layers skipped.Comment: ECCV 2018 Camera ready version. Code is available at https://github.com/ucbdrive/skipne

Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multi-grained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channel-wise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency or energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such trade-offs compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines

Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)

URNet : User-Resizable Residual Networks with Conditional Gating Module

Convolutional Neural Networks are widely used to process spatial scenes, but their computational cost is fixed and depends on the structure of the network used. There are methods to reduce the cost by compressing networks or varying its computational path dynamically according to the input image. However, since a user can not control the size of the learned model, it is difficult to respond dynamically if the amount of service requests suddenly increases. We propose User-Resizable Residual Networks (URNet), which allows users to adjust the scale of the network as needed during evaluation. URNet includes Conditional Gating Module (CGM) that determines the use of each residual block according to the input image and the desired scale. CGM is trained in a supervised manner using the newly proposed scale loss and its corresponding training methods. URNet can control the amount of computation according to user's demand without degrading the accuracy significantly. It can also be used as a general compression method by fixing the scale size during training. In the experiments on ImageNet, URNet based on ResNet-101 maintains the accuracy of the baseline even when resizing it to approximately 80% of the original network, and demonstrates only about 1% accuracy degradation when using about 65% of the computation.Comment: 12 page

효율적인 추론을 위한 하드웨어 친화적 신경망 구조 및 가속기 설계

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 이혁재.머신 러닝 (Machine Learning) 방법 중 현재 가장 주목받고 있는 딥러닝(Deep Learning)에 관한 연구들이 하드웨어와 소프트웨어 두 측면에서 모두 활발하게 진행되고 있다. 높은 성능을 유지하면서도 효율적으로 추론을 하기 위하여 모바일용 신경망 구조(Neural Network Architecture) 설계 및 학습된 모델 압축 등 소프트웨어 측면에서의 최적화 방법들이 연구되고 있으며, 이미 학습된 딥러닝 모델이 주어졌을 때 빠른 추론과 높은 에너지효율성을 갖는 가속기를 설계하는 하드웨어 측면에서의 연구가 동시에 진행되고 있다. 이러한 기존의 최적화 및 설계 방법에서 더 나아가 본 논문에서는 새로운 하드웨어 설계 기술과 모델 변환 방법 등을 적용하여 더 효율적인 추론 시스템을 만드는 것을 목표로 한다. 첫 번째, 새로운 하드웨어 설계 방법인 확률 컴퓨팅(Stochastic computing)을 도입하여 더 효율적인 딥러닝 가속 하드웨어를 설계하였다. 확률 컴퓨팅은 확률 연산에 기반을 둔 새로운 회로 설계 방법으로 기존의 이진 연산 회로(Binary system)보다 훨씬 더 적은 트랜지스터를 사용하여 동일한 연산 회로를 구현할 수 있다는 장점이 있다. 특히, 딥러닝에서 가장 많이 사용되는 곱셈 연산을 위하여 이진 연산 회로에서는 배열 승산기(Array Multiplier)를 필요로 하지만 확률 컴퓨팅에서는 AND 게이트하나로 구현이 가능하다. 선행 연구들이 확률 컴퓨팅 회로를 기반한 딥러닝 가속기들을 설계하고 있는데, 인식률이 이진 연산 회로에 비하여 많이 뒤쳐지는 결과를 보여주었다. 이러한 문제들을 해결하기 위하여 본 논문에서는 연산의 정확도를 더 높일 수 있도록 단극성 부호화(Unipolar encoding) 방법을 활용하여 가속기를 설계하였고, 확률 컴퓨팅 숫자 생성기 (Stochastic number generator)의 오버헤드를 줄이기 위하여 확률 컴퓨팅 숫자 생성기를 여러 개의 뉴런이 공유하는 방법을 제안하였다. 두 번째, 더 높은 추론 속도 향상을 위하여 학습된 딥러닝 모델을 압축하는 방법 대신에 신경망 구조를 변환 하는 방법을 제시하였다. 선행 연구들의 결과를 보면, 학습된 모델을 압축하는 방법을 최신 구조들에 적용하게 되면 가중치 파라미터(Weight Parameter)에는 높은 압축률을 보여주지만 실제 추론 속도 향상에는 미미한 효과를 보여주었다. 실질적인 속도 향상이 미흡한 것은 신경망 구조가 가지고 있는 구조상의 한계에서 발생하는 문제이고, 이것을 해결하려면 신경망 구조를 바꾸는것이 가장 근본적인 해결책이다. 이러한 관찰 결과를 토대로 본 논문에서는 선행연구보다 더 높은 속도 향상을 위하여 신경망 구조를 변환하는 방법을 제안하였다. 마지막으로, 각 층마다 서로 다른 구조를 가질 수 있도록 탐색 범위를 더 확장시키면서도 학습을 가능하게 하는 신경망 구조 탐색 방법을 제시하였다. 선행 연구에서의 신경망 구조 탐색은 기본 단위인 셀(Cell)의 구조를 탐색하고, 그 결과를 복사하여 하나의 큰 신경망으로 만드는 방법을 이용한다. 해당 방법은 하나의 셀 구조만 사용되기 때문에 위치에 따른 입력 특성맵(Input Feature Map)의 크기나 가중치 파라미터의 크기 등에 관한 정보는 무시하게 된다. 본 논문은 이러한 문제점들을 해결하면서도 안정적으로 학습을 시킬 수 있는 방법을 제시하였다. 또한, 연산량뿐만아니라 메모리 접근 횟수의 제약을 주어 더 효율적인 구조를 찾을 수 있도록 도와주는 페널티(Penalty)를 새로이 고안하였다.Deep learning is the most promising machine learning algorithm, and it is already used in real life. Actually, the latest smartphone use a neural network for better photograph and voice recognition. However, as the performance of the neural network improved, the hardware cost dramatically increases. Until the past few years, many researches focus on only a single side such as hardware or software, so its actual cost is hardly improved. Therefore, hardware and software co-optimization is needed to achieve further improvement. For this reason, this dissertation proposes the efficient inference system considering the hardware accelerator to the network architecture design. The first part of the dissertation is a deep neural network accelerator with stochastic computing. The main goal is the efficient stochastic computing hardware design for a convolutional neural network. It includes stochastic ReLU and optimized max function, which are key components in the convolutional neural network. To avoid the range limitation problem of stochastic numbers and increase the signal-to-noise ratio, we perform weight normalization and upscaling. In addition, to reduce the overhead of binary-to-stochastic conversion, we propose a scheme for sharing stochastic number generators among the neurons in the convolutional neural network. The second part of the dissertation is a neural architecture transformation. The network recasting is proposed, and it enables the network architecture transformation. The primary goal of this method is to accelerate the inference process through the transformation, but there can be many other practical applications. The method is based on block-wise recasting; it recasts each source block in a pre-trained teacher network to a target block in a student network. For the recasting, a target block is trained such that its output activation approximates that of the source block. Such a block-by-block recasting in a sequential manner transforms the network architecture while preserving accuracy. This method can be used to transform an arbitrary teacher network type to an arbitrary student network type. It can even generate a mixed-architecture network that consists of two or more types of block. The network recasting can generate a network with fewer parameters and/or activations, which reduce the inference time significantly. Naturally, it can be used for network compression by recasting a trained network into a smaller network of the same type. The third part of the dissertation is a fine-grained neural architecture search. InheritedNAS is the fine-grained architecture search method, and it uses the coarsegrained architecture that is found from the cell-based architecture search. Basically, fine-grained architecture has a very large search space, so it is hard to find directly. A stage independent search is proposed, and this method divides the entire network to several stages and trains each stage independently. To break the dependency between each stage, a two-point matching distillation method is also proposed. And then, operation pruning is applied to remove the unimportant operation. The block-wise pruning method is used to remove the operations rather than the node-wise pruning. In addition, a hardware-aware latency penalty is proposed, and it covers not only FLOPs but also memory access.1 Introduction 1 1.1 DNN Accelerator with Stochastic Computing 2 1.2 Neural Architecture Transformation 4 1.3 Fine-Grained Neural Architecture Search 6 2 Background 8 2.1 Stochastic Computing 8 2.2 Neural Network 10 2.2.1 Network Compression 10 2.2.2 Neural Network Accelerator 13 2.3 Knowledge Distillation 17 2.4 Neural Architecture Search 19 3 DNN Accelerator with Stochastic Computing 23 3.1 Motivation 23 3.1.1 Multiplication Error on Stochastic Computing 23 3.1.2 DNN with Stochastic Computing 24 3.2 Unipolar SC Hardware for CNN 25 3.2.1 Overall Hardware Design 25 3.2.2 Stochastic ReLU function 27 3.2.3 Stochastic Max function 30 3.2.4 Efficient Average Function 36 3.3 Weight Modulation for SC Hardware 38 3.3.1 Weight Normalization for SC 38 3.3.2 Weight Upscaling for Output Layer 43 3.4 Early Decision Termination 44 3.5 Stochastic Number Generator Sharing 49 3.6 Experiments 53 3.6.1 Accuracy of CNN using Unipolar SC 53 3.6.2 Synthesis Result 57 3.7 Summary 58 4 Neural Architecture Transformation 59 4.1 Motivation 59 4.2 Network Recasting 61 4.2.1 Recasting from DenseNet to ResNet and ConvNet 63 4.2.2 Recasting from ResNet to ConvNet 63 4.2.3 Compression 63 4.2.4 Block Training 65 4.2.5 Sequential Recasting and Fine-tuning 67 4.3 Experiments 69 4.3.1 Visualization of Filter Reduction 70 4.3.2 CIFAR 71 4.3.3 ILSVRC2012 73 4.4 Summary 76 5 Fine-Grained Neural Architecture Search 77 5.1 Motivation 77 5.1.1 Search Space Reduction Versus Diversity 77 5.1.2 Hardware-Aware Optimization 78 5.2 InheritedNAS 79 5.2.1 Stage Independent Search 79 5.2.2 Operation Pruning 82 5.2.3 Entire Search Procedure 83 5.3 Hardware-aware Penalty Design 85 5.4 Experiments 87 5.4.1 Fine-Grained Architecture Search 88 5.4.2 Penalty Analysis 90 5.5 Summary 92 6 Conclusion 93 Abstract (In Korean) 113Docto