NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications (1804.03230v2)

Abstract: This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily reduce the direct metrics, such as latency and energy consumption. To solve this problem, NetAdapt incorporates direct metrics into its adaptation algorithm. These direct metrics are evaluated using empirical measurements, so that detailed knowledge of the platform and toolchain is not required. NetAdapt automatically and progressively simplifies a pre-trained network until the resource budget is met while maximizing the accuracy. Experiment results show that NetAdapt achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU, compared with the state-of-the-art automated network simplification algorithms. For image classification on the ImageNet dataset, NetAdapt achieves up to a 1.7$\times$ speedup in measured inference latency with equal or higher accuracy on MobileNets (V1&V2).

• The paper introduces an algorithm that directly measures performance metrics to optimize DNNs for mobile resource constraints.
• The methodology iteratively refines network proposals based on platform-specific evaluations, achieving up to a 1.7× latency reduction with maintained accuracy.
• The approach automates network adaptation without requiring detailed hardware insights, outperforming methods like MorphNet and ADC.

Overview of NetAdapt for Mobile Neural Network Adaptation

The paper "NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications" introduces an algorithm called NetAdapt. This algorithm addresses the challenge of deploying deep neural networks (DNNs) on resource-constrained platforms such as mobile devices. Traditional approaches optimize DNNs using indirect metrics like the number of multiply-accumulate operations (MACs) or the number of weights, which may not accurately reduce real-world metrics such as latency and energy consumption. NetAdapt overcomes this limitation by incorporating direct metrics into its optimization algorithm.

Methodology

NetAdapt utilizes empirical measurements rather than platform-specific knowledge to evaluate direct metrics, allowing for platform-aware adaptation without requiring detailed hardware knowledge. This is pragmatic given the proprietary nature of modern systems. The optimization proceeds iteratively, simplifying a pre-trained network to meet a specified resource budget while maximizing accuracy. Each iteration involves generating network proposals that are successively evaluated on the target platform, refining them until the resource constraints are satisfied.

Key Contributions

The paper's principal contributions include:

1. Framework for Direct Metrics: NetAdapt eschews indirect metrics for direct ones in optimizing networks to meet specific resource budgets using empirical measurements.
2. Automated Optimization Algorithm: The algorithm outperforms state-of-the-art network simplification techniques, such as MorphNet and ADC, achieving up to a 1.7× reduction in latency on mobile devices with equal or higher accuracy.
3. Empirical Results: The algorithm's efficacy is demonstrated on various networks and platforms, including small networks like MobileNetV1, showing improved trade-offs between latency and accuracy.

Experimental Results

NetAdapt's performance is benchmarked against three leading network simplification methods: Multipliers, MorphNet, and ADC. Notably, when applied to MobileNetV1, NetAdapt achieves a 1.7× speed-up over the multipliers with similar or improved accuracy. Further, it demonstrates a substantial latency reduction in adapting networks, validating the importance of direct metrics in network optimization.

Analysis

The paper provides a thorough analysis of the algorithm's components, such as short- and long-term fine-tuning's impact on accuracy and the importance of appropriate resource reduction schedules. It illustrates that employing empirical measurements for direct metrics can lead to more efficient and accurate network deployment on resource-limited platforms.

Implications and Future Directions

NetAdapt's approach of using empirical evaluations aligns network simplification with practical performance improvements, which is critical for mobile AI applications. This methodology enables adapting DNNs without necessitating hardware-specific knowledge while remaining extensible across various metrics. Future research may build on this by exploring other direct metrics or enhancing the adaptation process with more sophisticated layer-wise customization.

In conclusion, NetAdapt represents a significant step in platform-aware neural network deployment, offering a robust framework for optimizing networks to operate efficiently under the constraints of mobile environments. The method’s adaptability and precision underscore the potential for further advancements in the optimization of DNNs for diverse deployment scenarios.