Neural Architecture Design for GPU-Efficient Networks (2006.14090v4)

Abstract: Many mission-critical systems are based on GPU for inference. It requires not only high recognition accuracy but also low latency in responding time. Although many studies are devoted to optimizing the structure of deep models for efficient inference, most of them do not leverage the architecture of \textbf{modern GPU} for fast inference, leading to suboptimal performance. To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies. This design principle enables us to search for GPU-efficient network structures effectively by a simple and lightweight method as opposed to most Neural Architecture Search (NAS) methods that are complicated and computationally expensive. Based on the proposed framework, we design a family of GPU-Efficient Networks, or GENets in short. We did extensive evaluations on multiple GPU platforms and inference engines. While achieving $\geq 81.3\%$ top-1 accuracy on ImageNet, GENet is up to $6.4$ times faster than EfficienNet on GPU. It also outperforms most state-of-the-art models that are more efficient than EfficientNet in high precision regimes. Our source code and pre-trained models are available from \url{https://github.com/idstcv/GPU-Efficient-Networks}.

• The paper introduces a novel hybrid design that adaptively selects convolutional operators across network layers to optimize GPU inference performance.
• It employs extensive profiling to link batch size, FLOP count, and kernel characteristics with latency, guiding efficient architecture choices.
• Empirical results show GENets achieving up to 6.4x faster inference speed while maintaining competitive top-1 accuracy on ImageNet benchmarks.

Analyzing GPU-Efficient Network Architectures

The paper "Neural Architecture Design for GPU-Efficient Networks" presents a novel approach focused on designing neural networks optimized for GPU efficiency, while maintaining competitive accuracy levels. The authors introduce a simplified design principle based on extensive empirical studies rather than computationally intensive Neural Architecture Search (NAS) methods. Their proposed methodology showcases a novel hybrid network architecture that adaptively integrates different convolutional operators at different depths, optimizing inference performance across modern GPU platforms.

Key Insights

The fundamental insight underpinning this research is the differential efficiency of various convolutional mechanisms—specifically, full convolutions versus depth-wise and bottleneck convolutions—when executed on GPU hardware. Modern GPUs are characterized by their high core count and substantial memory, leading to complexities beyond simple FLOP and model size metrics in predicting inference latency. The paper identifies that lower network layers favor full convolutions, whereas depth-wise convolutions and bottleneck structures are more efficient in upper layers. This adaptability is attributed to the varying singular value distribution and intrinsic rank of convolutional kernel layers across the network architecture.

Methodological Approach

• Network Profiling and Design: Extensive profiling experiments were conducted to understand the GPU performance characteristics of various convolutional blocks. Profiling was extended to measure how inference latency correlates with batch size, FLOP count, and model size. Based on these metrics, specific depths of network layers were designed to use an efficient convolutional operator suitable for the layer's requirements.
• Genetic Network Design: The authors proposed a network series called GPU-Efficient Networks (GENets) through a combination of manual design and Local Linear Regression NAS (LLR-NAS). The networks were optimized to offer varying performance-accuracy trade-offs within specified latency budgets.
• Empirical Validation: GENets were empirically validated against multiple benchmarks with significant enhancements in inference speed without compromising accuracy. The GENet models notably outperformed EfficiencyNet, achieving up to 6.4 times faster inference speed while maintaining or exceeding top-1 accuracy on ImageNet.

Quantitative Performance Impact

The GENet models demonstrated:

• Speed and Efficiency: A drastic increase in speed, achieving up to 6.4x faster inference compared to state-of-the-art models while maintaining high accuracy.
• Accuracy Benchmarks: Top-1 accuracies such as 81.3% on ImageNet, which is competitive with models like EfficientNet-B3 but with enhanced speed capabilities.

Theoretical and Practical Implications

The methodology that combines architectural insights with lightweight NAS optimizes the balance between recognition accuracy and inference latency on GPU platforms, which is pivotal for mission-critical systems requiring rapid response times. Practically, this has significant implications in AI implementations for real-time applications, such as autonomous driving, real-time language translation, and video analytics.

Future Speculations

Future developments may extend this architecture to specialize further with evolving GPU hardware architectures, possibly integrating advanced NAS methodologies or exploring novel convolutional block structures like transformers, given their rising prominence in deep learning architectures. Furthermore, the application domains can increase conversely by expanding models into edge devices where power efficiency could be balanced with GPU efficiency.

The paper offers substantial groundwork in redefining efficient network design, particularly emphasizing GPU-specific optimizations. Thus, it provides both academic researchers and industry practitioners with critical insights into maximizing the effective use of contemporary hardware accelerators in advanced neural network models.