Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution (1701.00299v3)

Abstract: We introduce Dynamic Deep Neural Networks (D2NN), a new type of feed-forward deep neural network that allows selective execution. Given an input, only a subset of D2NN neurons are executed, and the particular subset is determined by the D2NN itself. By pruning unnecessary computation depending on input, D2NNs provide a way to improve computational efficiency. To achieve dynamic selective execution, a D2NN augments a feed-forward deep neural network (directed acyclic graph of differentiable modules) with controller modules. Each controller module is a sub-network whose output is a decision that controls whether other modules can execute. A D2NN is trained end to end. Both regular and controller modules in a D2NN are learnable and are jointly trained to optimize both accuracy and efficiency. Such training is achieved by integrating backpropagation with reinforcement learning. With extensive experiments of various D2NN architectures on image classification tasks, we demonstrate that D2NNs are general and flexible, and can effectively optimize accuracy-efficiency trade-offs.

• The paper introduces Dynamic Deep Neural Networks (D 2NNs), a novel architecture with integrated control modules that dynamically activate only necessary network components based on input data to optimize accuracy-efficiency trade-offs.
• Key distinctions include end-to-end learning of execution decisions, architectural flexibility, and direct optimization of custom and global efficiency metrics, unlike prior methods.
• Trained using backpropagation and reinforcement learning, D 2NNs are experimentally validated on image classification tasks, demonstrating significant computational savings with minimal accuracy loss across various architectures and environments.

Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution

The paper "Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution" introduces a novel architectural concept for deep neural networks aimed at enhancing computational efficiency through selective execution strategies. The researchers propose the Dynamic Deep Neural Network (D$^2$NN), an advanced feed-forward network variation distinguished by its ability to dynamically execute only a necessary subset of neurons based on input data characteristics, thus balancing the trade-offs between accuracy and computational efficiency.

Key Contributions and Findings

One of the central contributions of this work is the introduction of control modules within the traditional architecture of deep neural networks, allowing selective execution. These control modules, integrated as sub-networks, generate decisions that determine which parts of the network should be activated during inference. The D$^2$NN framework allows each input to follow a path dynamically determined by its nature, thus avoiding unnecessary computations present in conventional neural networks, which operate on static execution sequences.

This research highlights four primary distinctions from prior methods addressing computational efficiency:

1. End-to-End Learning of Selective Execution Decisions: Unlike traditional approaches that separate the learning of feature representations and execution controls, D$^2$NNs train both simultaneously, leading to more integrated optimization.
2. Architectural Flexibility: The design supports varied network structures, including parallel paths, and can be tailored to specific application requirements.
3. Direct Optimization of Efficiency Metrics: Users can define custom efficiency metrics shaped to their needs, offering broader applications than methods limited to indirect efficiency improvement through sparsity constraints.
4. Global Metric Optimization: D$^2$NNs uniquely optimize non-decomposable global performance metrics such as the F-score, a challenge not directly addressed by preceding works.

Methodology and Experimental Evaluation

The training of D$^2$NNs leverages both backpropagation and reinforcement learning techniques to navigate the non-differentiable aspects of the control modules. The researchers detail a mini-bag strategy to handle set-based accuracy metrics effectively, allowing the network to learn efficiency-accuracy trade-offs directly within a formed multi-example context.

Extensive experiments, particularly in the domain of image classification, validate the efficacy and flexibility of D$^2$NNs. Various architectural designs based on the D$^2$NN framework, such as high-low capacity networks, cascades, chains, and hierarchical networks, demonstrate the ability to make significant reductions in computational demand with minimal impact on accuracy across different computational environments, ranging from mobile devices to large-scale data centers.

Implications and Future Directions

This work presents significant implications for the deployment of deep neural networks in resource-constrained environments. The ability to optimize dynamically and tailor execution paths per input is advantageous, especially in applications requiring real-time processing with minimized resource expenditure.

Future research may delve into enhancing the decision-making algorithms within the control modules for even finer-grained execution control. Additionally, exploration into the integration of D$^2$NNs with other forms of neural network efficiencies such as pruning, quantization, or transfer learning could provide further benefits.

In conclusion, Dynamic Deep Neural Networks represent an important evolutionary step in neural network design, allowing practitioners to achieve precise control over computational costs without sacrificing performance quality. This approach offers a robust framework for building highly efficient neural networks adaptable to diverse technological landscapes and requirements.