Adaptive Neural Networks for Efficient Inference (1702.07811v2)

Abstract: We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example. By allowing examples correctly classified using early layers of the system to exit, we avoid the computational time associated with full evaluation of the network. We extend this to learn a network selection system that adaptively selects the network to be evaluated for each example. We show that computational time can be dramatically reduced by exploiting the fact that many examples can be correctly classified using relatively efficient networks and that complex, computationally costly networks are only necessary for a small fraction of examples. We pose a global objective for learning an adaptive early exit or network selection policy and solve it by reducing the policy learning problem to a layer-by-layer weighted binary classification problem. Empirically, these approaches yield dramatic reductions in computational cost, with up to a 2.8x speedup on state-of-the-art networks from the ImageNet image recognition challenge with minimal (<1%) loss of top5 accuracy.

• The paper presents adaptive strategies, including early-exit and network selection, to dynamically reduce computation during inference.
• It demonstrates up to a 2.8x reduction in test-time evaluation with less than 1% loss in top-5 accuracy on the ImageNet benchmark.
• The approach enables efficient DNN deployment in resource-constrained settings such as IoT and mobile applications, broadening AI usability.

Adaptive Neural Networks for Efficient Inference

The paper "Adaptive Neural Networks for Efficient Inference" by Bolukbasi, Wang, Dekel, and Saligrama proposes a novel framework to enhance the computational efficiency of deep neural networks (DNNs) during inference, while maintaining competitive accuracy levels. In recent years, DNNs have witnessed significant advancements that have resulted in improved accuracy across various applications, notably in domains like image and speech recognition. However, such improvements are often accompanied by substantial increases in computational costs, particularly during test-time evaluation. This work addresses this challenge through an adaptive utilization approach that adjusts computational resource allocation based on the complexity of the input data.

Research Contributions

The authors introduce two primary adaptive mechanisms: an early-exit strategy and an adaptive network selection method.

1. Early-exit Strategy: The strategy involves training an exit policy for each decision point in the network, where the policy decides whether the current prediction is reliable enough to terminate computation early or if further evaluations by deeper layers in the network are warranted. This layer-by-layer evaluation yields considerable reductions in evaluation time for "easy" examples without compromising accuracy significantly.
2. Adaptive Network Selection: This method extends the adaptive evaluation concept beyond individual networks by selecting between multiple pre-trained networks based on resource-efficiency versus accuracy trade-offs. By arranging networks of varying complexity in a directed acyclic graph, the framework allows for less complex models to handle less challenging examples, while reserving complex models for more difficult cases that demand their high computational power.

Empirical Evaluation

The research demonstrates empirical gains using the ImageNet dataset, an acclaimed benchmark in image recognition. The proposed techniques achieved up to a 2.8x reduction in average test-time evaluation with minimal ($<1\%$) loss in top-5 accuracy. Notably, the adaptive network selection policy closely approached the performance of an oracle policy, signifying the potential of learned policies to emulate expert decision-making without true label insight during inference.

Implications and Future Directions

From a practical standpoint, the proposed techniques present a viable pathway for deploying high-performing DNNs in resource-constrained environments such as IoT devices and mobile applications. By dynamically adjusting computational workloads, the framework not only promises significant cost savings but also extends the applicability of sophisticated ML models to settings previously constrained by hardware limitations.

This adaptive inference paradigm opens several avenues for future work. There is potential for integrating these strategies into training processes, allowing networks to be inherently optimized for conditional computation. Further exploration into adaptive frameworks might benefit real-time streaming applications where latency is critical, such as autonomous vehicles and interactive AI systems.

Conclusion

The work presented in this paper substantiates the feasibility of adaptively utilizing DNNs for efficient inference. By strategically balancing computation and accuracy, the proposed methods contribute to the broader pursuit of sustainable and scalable deployment of AI technologies. As the field advances, such adaptive methodologies will likely become integral to the development and implementation of future intelligent systems.