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Dynamically throttleable neural networks

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Abstract

Conditional computation for deep neural networks reduces overall computational load and improves model accuracy by running a subset of the network. In this work, we present a runtime dynamically throttleable neural network (DTNN) that can self-regulate its own performance target and computing resources by dynamically activating neurons in response to a single control signal, called utilization. We describe a generic formulation of throttleable neural networks (TNNs) by grouping and gating partial neural modules with various gating strategies. To directly optimize arbitrary application-level performance metrics and model complexity, a controller network is trained separately to predict a context-aware utilization via deep contextual bandits. Extensive experiments and comparisons on image classification and object detection tasks show that TNNs can be effectively throttled across a wide range of utilization settings, while having peak accuracy and lower cost that are comparable to corresponding vanilla architectures such as VGG, ResNet, ResNeXt, and DenseNet. We further demonstrate the effectiveness of the controller network on throttleable 3D convolutional networks (C3D) for video-based hand gesture recognition, which outperforms the vanilla C3D and all fixed utilization settings.

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Authors and Affiliations

  1. University of California, Riverside, Riverside, 92521, CA, USA

    Hengyue Liu & Bir Bhanu

  2. University of California, Berkeley, Berkeley, 94720, CA, USA

    Samyak Parajuli

  3. SRI International, Princeton, NJ, 08540, USA

    Jesse Hostetler

  4. Latent AI, Skillman, 08558, NJ, USA

    Sek Chai

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  1. Hengyue Liu
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Appendix A

Appendix A

1.1 A.1 FLOPs calculation

We use the codes from [85] for computing the FLOPs. Table 3 summarizes how to compute the FLOPs for some common layers:

For computing FLOPs of a convolutional layer in the TNN, we count how many activated kernels within each layer, and the FLOPs are calculated as 2 \(\times \) No. activated kernels \(\times \) kernel shape \(\times \) output shape. For fully connected layers, the input size will change, and the FLOPs are calculated as 2 \(\times \) activated input size \(\times \) output size.

Table 3 FLOPs calculation for common layers
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Table 4 Detailed architectures of the DTNN for video-based hand gesture recognition on the Jester dataset
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Table 5 Contextual controller architecture based on 3D-ShuffleNet
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1.2 A.2 Detailed architectures

Table 4 presents the exact specification of the C3D-W used for hand gesture recognition. The conv-block consists of a 3D convolutional layer with ReLU activation. Stride of all convolutional layers is \(1 \times 1 \times 1\). Stride of max-pool-1 is \(1 \times 2 \times 2\), and other max-pool layers are \(2 \times 2 \times 2\). Padding of all conv-blocks is one, and padding of all max-pool layers is zero. In total, C3D-WN has 2.654G FLOPs of non-gated operations, and 94.752G FLOPs of throttleable operations. The detailed architecture of the controller is illustrated in Table 5. The network begins with a convolutional layer followed by 16 ShuffleNet units grouped into 3 stages (conv2_x to conv4_x). Each ShuffleNet unit is a residual block where the residual branch consists of one \(1 \times 1 \times 1\) group convolution, channel shuffle operation, \( 3 \times 3 \times 3\) depthwise convolution [1], and \(1 \times 1 \times 1\) group convolution. A cost comparison of the TNN and controller is shown in Table 6, indicating that the controller is much more computationally efficient.

1.3 A.3 Class distribution of 20BN-JESTER

The class distribution of the 20BN-JESTER training set is shown in Fig. 13.

Table 6 Cost comparison between the data path network (C3D-WN) and controller (3D-ShuffleNet)
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Fig. 13
figure 13

Class distribution of 20BN-JESTER training set

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Liu, H., Parajuli, S., Hostetler, J. et al. Dynamically throttleable neural networks. Machine Vision and Applications 33, 59 (2022). https://doi.org/10.1007/s00138-022-01311-z

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