Optimizing Energy-Efficient Inference of Dynamic Deep Neural Networks over Multi-Tiered Interconnected Systems

Temel Kavramlar

The core message of this article is to devise an efficient solution framework, called Feasible Inference Graph (FIN), for allocating blocks of dynamic deep neural networks (DNNs) with early exits to the nodes of a multi-tiered mobile-edge-cloud system in order to minimize the overall energy consumption while satisfying application-specific inference requirements and system constraints.

Özet

The article addresses the problem of efficiently deploying dynamic deep neural networks (DNNs) with early exits over a multi-tiered mobile-edge-cloud system. The key highlights are:

The authors formulate an optimization problem to allocate DNN blocks to system nodes in order to minimize the overall energy consumption, while satisfying application-specific inference requirements (accuracy and latency) and system constraints (bandwidth and computing capacity).

They propose a solution framework called Feasible Inference Graph (FIN) that manipulates a graph representation of the DNN block allocation problem to create a specialized graph that only contains feasible solutions. The optimal allocation is then found by computing the minimum-cost path on this graph.

The authors evaluate FIN's performance using three DNN models with early exits (B-LeNet, B-AlexNet, B-ResNet) trained on different datasets. The results show that FIN can achieve over 65% energy savings compared to a state-of-the-art technique, while closely matching the optimal solution.

In a multi-application scenario, FIN is shown to outperform the benchmark in terms of energy consumption, success probability of meeting application requirements, and the ability to leverage early exits across the mobile-edge-cloud tiers.

İstatistikler

The article provides the following key metrics and figures:

The feature map and complexity of the DNN model blocks in terms of number of input features and millions of operations (MOPs) (Table III).
The inference accuracy of the pre-trained DNN models with early exits (Table IV).
The communication capacity and energy consumption parameters of the mobile, edge, and cloud nodes (Table V).

Alıntılar

"By controlling the splitting points defining the sections of the DNN, one can control the computing load allocated to the different devices/servers as well as the amount of data transmitted on the communication links connecting them."
"Our work focuses on the problem of allocating "blocks" of layers of DNNs with early exits to the nodes composing the overall mobile-edge-cloud system."
"Our results show that models equipped with early exits can dramatically decrease the overall energy consumption when some of these exits are allocated to mobile or edge devices under system and application-level constraints, by reducing the involvement of larger-scale nodes in the completion of the inference."

Önemli Bilgiler Şuradan Elde Edildi

Resource-aware Deployment of Dynamic DNNs over Multi-tiered Interconnected Systems

by Chetna Singh... : arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08060.pdf

Daha Derin Sorular

How can the FIN framework be extended to handle dynamic changes in the network conditions and application requirements during runtime

To extend the FIN framework to handle dynamic changes in network conditions and application requirements during runtime, several key enhancements can be implemented:

Dynamic Reconfiguration: The framework can incorporate algorithms that continuously monitor network conditions, such as bandwidth availability and latency, and dynamically reconfigure the DNN deployment based on real-time data. This could involve reallocating DNN blocks to different nodes or adjusting the early-exit points to optimize energy efficiency while meeting application requirements.

Adaptive Decision-Making: Implementing adaptive decision-making algorithms that can adjust the DNN deployment strategy based on changing network conditions and application demands. These algorithms could use reinforcement learning or other AI techniques to learn and adapt to evolving scenarios.

Predictive Analytics: Utilizing predictive analytics to forecast potential changes in network conditions and application requirements, enabling proactive adjustments to the DNN deployment before issues arise. This could involve machine learning models that predict future network performance based on historical data.

Feedback Mechanisms: Implementing feedback mechanisms that provide information on the effectiveness of the current DNN deployment strategy. This feedback can be used to continuously optimize the deployment in response to changing conditions.

Dynamic Resource Allocation: Developing algorithms that can dynamically allocate resources based on the current workload and system conditions. This could involve load balancing techniques to distribute inference tasks efficiently across the network nodes.

By incorporating these enhancements, the FIN framework can adapt to dynamic changes in network conditions and application requirements in real-time, ensuring optimal performance and energy efficiency.

What are the potential trade-offs between energy efficiency, inference accuracy, and latency when deploying early-exit DNN models on heterogeneous mobile-edge-cloud systems

When deploying early-exit DNN models on heterogeneous mobile-edge-cloud systems, there are several potential trade-offs between energy efficiency, inference accuracy, and latency:

Energy Efficiency vs. Inference Accuracy: Deploying early-exit DNN models with more exits can improve energy efficiency by allowing for early termination of inference tasks. However, this may come at the cost of reduced inference accuracy, as early exits may not capture the full complexity of the input data, leading to lower accuracy levels.

Latency vs. Inference Accuracy: Increasing the number of early exits in DNN models can reduce latency by enabling quicker decisions to be made at different points in the network. However, this may impact inference accuracy, as early exits may not have the capacity to process complex data effectively, leading to potential accuracy trade-offs.

Resource Allocation: Balancing the allocation of DNN blocks across mobile, edge, and cloud nodes to optimize energy efficiency, inference accuracy, and latency. This involves considering the computational capabilities of each node, communication bandwidth, and the specific requirements of the applications being deployed.

System Complexity: Managing the complexity of deploying and orchestrating dynamic DNN models across a multi-tiered network. This complexity can impact the overall system architecture, resource utilization, and the ability to meet stringent latency and accuracy requirements.

By carefully considering these trade-offs and optimizing the deployment of early-exit DNN models, it is possible to achieve a balance between energy efficiency, inference accuracy, and latency in heterogeneous mobile-edge-cloud systems.

What are the implications of the FIN approach on the overall system architecture and the role of the orchestrator in managing the distributed inference tasks

The FIN approach has several implications on the overall system architecture and the role of the orchestrator in managing distributed inference tasks:

System Architecture: The FIN framework introduces a dynamic and adaptive approach to deploying DNN models across multi-tiered networks. This requires a flexible system architecture that can accommodate changes in network conditions, application requirements, and resource availability. The architecture needs to support real-time decision-making and resource allocation to optimize energy efficiency and inference performance.

Orchestrator Role: The orchestrator plays a crucial role in managing the distributed inference tasks by coordinating the allocation of DNN blocks to network nodes, monitoring network conditions, and adjusting the deployment strategy in response to changing requirements. The orchestrator acts as the central decision-maker in optimizing energy consumption, latency, and accuracy while ensuring efficient utilization of resources.

Resource Management: The orchestrator is responsible for efficient resource management, including load balancing, task scheduling, and dynamic allocation of computational and communication resources. By intelligently distributing inference tasks across mobile, edge, and cloud nodes, the orchestrator can maximize system performance and energy efficiency.

Scalability and Flexibility: The FIN approach enhances the scalability and flexibility of the system architecture by enabling dynamic adjustments to the DNN deployment based on real-time data. This allows the system to adapt to changing conditions, handle varying workloads, and meet the diverse requirements of multiple applications running on the network.

Overall, the FIN framework transforms the system architecture by introducing a data-driven and adaptive approach to deploying dynamic DNN models, with the orchestrator playing a central role in optimizing performance and resource utilization in heterogeneous mobile-edge-cloud systems.