ML Model Layer Offloading for Edge Power Optimization
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Solution Overview
Problem
In edge computing, resource-constrained devices face challenges in efficiently executing machine learning models due to limited compute, memory, and power resources, particularly in scenarios where power availability and workload fluctuations are significant, such as in solar-powered wireless base stations.
Innovation Solution
The approach involves determining which layers of a machine learning model to execute locally versus remotely based on available resources and telemetry data, allowing for the offloading of resource-intensive tasks to a remote node while reducing bandwidth requirements by transmitting intermediate results, thereby optimizing power consumption and execution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning models are executed locally on resource-constrained edge devices, then processing speed and responsiveness are improved, but power consumption and resource utilization worsen
Solution Approach 1:
The machine learning model is divided into multiple layers, with some layers executed locally on the edge device and other layers executed remotely on cloud servers. This segmentation allows the system to balance between local processing speed and remote power consumption, executing only necessary model layers locally while offloading computationally intensive layers to the cloud.
Solution Approach 2:
The system dynamically determines which model layers to execute locally versus remotely based on real-time resource conditions, including available power, compute capacity, and network status. This dynamic adaptation allows the edge device to optimize the trade-off between processing speed and power consumption under varying operating conditions.
2Reliability
If more compute resources are allocated locally for machine learning execution, then model execution accuracy and service level agreement compliance are improved, but device complexity and resource requirements worsen
Solution Approach 1:
By segmenting the machine learning model into executable layers that can be distributed between edge device and cloud server, the system maintains high reliability and service level agreement compliance without requiring the entire model to be executed locally. This reduces device complexity while ensuring critical processing functions are performed.
Solution Approach 2:
The edge device is designed with multi-functionality, capable of executing both local model layers and coordinating with remote cloud resources. This universal approach allows the same device to operate in different modes depending on resource availability, maintaining reliability without permanently increasing device complexity.
3Use of energy by moving object
If resource-intensive machine learning tasks are offloaded to remote nodes, then local power consumption is reduced, but bandwidth requirements and network dependency increase
Solution Approach 1:
The model is segmented into layers that are strategically placed between edge and cloud based on their computational requirements and data characteristics. This segmentation optimizes the balance between reducing local power consumption and minimizing bandwidth requirements by keeping data-intensive layers local while offloading compute-intensive layers to the cloud.
Solution Approach 2:
Data preprocessing and feature extraction are performed locally at the edge device before transmitting data to remote nodes for model inference. This preliminary action reduces the size and complexity of data that needs to be transmitted over the network, thereby reducing bandwidth requirements while still enabling effective use of remote compute resources.
Data Source
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AI summary
Methods, apparatus, systems and articles of manufacture to offload execution of a portion of a machine learning model are disclosed. An example apparatus includes processor circuitry to instantiate offload controller circuitry to select a first portion of layers of the machine learning model for execution at a first node and a second portion of the layers for remote execution for execution at a second node, model executor circuitry to execute the first portion of the layers, serialization circuitry to serialize the output of the execution of the first portion of the layers, and a network interface to transmit a request for execution of the machine learning model to the second node, the request including the serialized output of the execution of the first portion of the layers of the machine learning model and a layer identifier identifying the second portion of the layers of the machine learning model.