On the Efficiency of Convolutional Neural Networks
(2404.03617)Abstract
Since the breakthrough performance of AlexNet in 2012, convolutional neural networks (convnets) have grown into extremely powerful vision models. Deep learning researchers have used convnets to produce accurate results that were unachievable a decade ago. Yet computer scientists make computational efficiency their primary objective. Accuracy with exorbitant cost is not acceptable; an algorithm must also minimize its computational requirements. Confronted with the daunting computation that convnets use, deep learning researchers also became interested in efficiency. Researchers applied tremendous effort to find the convnet architectures that have the greatest efficiency. However, skepticism grew among researchers and engineers alike about the relevance of arithmetic complexity. Contrary to the prevailing view that latency and arithmetic complexity are irreconcilable, a simple formula relates both through computational efficiency. This insight enabled us to co-optimize the separate factors that determine latency. We observed that the degenerate conv2d layers that produce the best accuracy-complexity trade-off also have low operational intensity. Therefore, kernels that implement these layers use significant memory resources. We solved this optimization problem with block-fusion kernels that implement all layers of a residual block, thereby creating temporal locality, avoiding communication, and reducing workspace size. Our ConvFirst model with block-fusion kernels ran approximately four times as fast as the ConvNeXt baseline with PyTorch Inductor, at equal accuracy on the ImageNet-1K classification task. Our unified approach to convnet efficiency envisions a new era of models and kernels that achieve greater accuracy at lower cost.
Overview
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The paper discusses improving the efficiency of convolutional neural networks (convnets) through a novel block-fusion strategy, culminating in the ConvFirstNet model.
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It introduces robust metrics for measuring both model efficiency and computational efficiency, offering a clearer insight into the performance of convnets.
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The block-fusion kernels aim to optimize the execution of degenerate conv2d layers, which are common bottlenecks, thereby improving computational efficiency without sacrificing model efficiency.
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ConvFirstNet demonstrates superior performance in both model and computational efficiency compared to current leading models, suggesting the significant potential of block-fusion kernels in enhancing convnet efficiency.
Exploring the Efficiency of Convolutional Neural Networks through Block-Fusion Kernels
Introduction
The efficiency of convolutional neural networks (convnets) is a critical aspect of their deployment for inference, especially in resource-constrained environments. Recent developments in convnet architecture have pursued improved model efficiency through architectural innovations and expanded hyperparameters. Nevertheless, the computational efficiency—how fast a model can run on specific hardware with given software—remains paramount. This exploration unveils insights into enhancing convnet efficiency by co-optimizing model architecture and computational strategy through block-fusion kernels, culminating in the introduction of ConvFirstNet.
Measuring Efficiency
A foundational step in enhancing convnet efficiency is establishing robust metrics for both model and computational efficiency. Model efficiency, denoted by (\mathscr{E}_m(n)), quantifies the accuracy to computation (operations) ratio, reflecting the model's capability to deliver higher accuracy per compute operation. Computational efficiency, symbolized as (\mathscr{C}(n)), contrasts actual versus peak arithmetic throughput, spotlighting the hardware and algorithm's efficacy in utilizing available compute resources. Bridging these metrics, this study introduces the efficiency gap plot, offering a novel visualization of how model efficiency translates into actual latency or inference time, underpinned by the computational efficiency of the deployment environment.
Block Fusion: A Path to Computational Efficiency
At the heart of our approach is the block-fusion strategy, aimed at optimizing the execution of degenerate \lstinline[language=C]!conv2d! layers, which form the backbone of modern convnets. Degenerate layers, characterized by their reduced operational intensity, often become bottlenecks in computational efficiency. By designing block-fusion kernels that merge multiple layers into a singular, compute-intensive operation, we strategically improve the computational efficiency without compromising model efficiency. To illustrate and design these kernels, we introduce "tensor machines" – a conceptual model that simplifies understanding and developing these complex kernels.
ConvFirstNet: A Case Study in Efficiency
Leveraging the block-fusion strategy, we present ConvFirstNet, a convnet model that judiciously employs ConvFirst and MBConv blocks. ConvFirst blocks are utilized in the initial layers for their lightweight computational footprint, while MBConv blocks, enriched with Squeeze & Excitation layers, are deployed in deeper layers to ensure robust feature extraction with higher capacity. Our analysis, underpinned by both waterline and efficiency gap plots, reveals that ConvFirstNet, when executed with block-fusion kernels, markedly outperforms traditional layer-wise execution in computational efficiency, achieving higher frame rates at equivalent or superior accuracy levels.
Performance and Implications
The benchmarks of ConvFirstNet, alongside comparative analyses with EfficientNet and ConvNeXt models, underscore significant advancements in both model and computational efficiency. ConvFirstNet, with block-fusion kernels, not only achieves superior model efficiency but also excels in computational efficiency, demonstrating faster inference times than some of the leading models. These findings suggest that the co-optimization of model architecture and computational strategy—specifically through block-fusion kernels—holds substantial promise in advancing the performance and efficiency of convnets.
Conclusion
The exploration into ConvFirstNet underscores the pivotal role of computational strategy, alongside architectural innovation, in enhancing convnet efficiency. By introducing and applying the block-fusion approach, complemented by robust metrics and visualizations such as the efficiency gap and waterline analyses, we advocate for a holistic perspective on convnet optimization. This dual focus on model architecture and computational execution opens new avenues for research and development, propelling forward the efficacy and applicability of convnets across diverse and resource-constrained deployment environments.
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