Source Adobe Stock By Yasiru # 810835118
Background:
The computational demands of deep neural networks (DNNs) for computer vision (CV) tasks pose significant challenges for mobile devices, which are designed to be lightweight, small, and energy-efficient. To address this, mobile devices typically offload these tasks to edge devices via radio channels. While this method conserves energy and resources on mobile devices, the increasing complexity and demand for CV applications require more efficient resource management at the edge. Current approaches to offloading DNN tasks often overlook critical factors such as memory consumption of DNN models and the structural correlations between different CV tasks, leading to suboptimal resource utilization and increased latency. Furthermore, existing methods fail to adequately fine-tune DNN layers for specific tasks, resulting in inefficient use of computational and radio resources. These limitations hinder the ability to meet the accuracy and latency requirements of offloaded tasks while minimizing resource consumption, underscoring the need for more holistic optimization strategies.
Description:
Northeastern researchers have developed OffloaDNN, a framework designed to optimize the execution of Computer Vision (CV) tasks on mobile devices using deep neural networks (DNNs). OffloaDNN addresses critical issues like memory consumption and structural correlation between DNN models, which are often overlooked in current approaches. By optimizing radio and compute resources, the DNN structure, and the offloading process, OffloaDNN employs techniques such as sharing lower-level DNN layers across tasks, fine-tuning layers for specific tasks, and pruning to reduce memory use while maintaining accuracy. The framework, formulated as the DNN for scalable Offloading of Tasks (DOT) problem, minimizes task rejection and resource consumption while meeting accuracy and latency requirements. Demonstrated to handle 26.9% more tasks and reduce memory use by 82.5% and per-inference computing time by 77.4% compared to current methods, OffloaDNN proves to be an efficient and scalable solution for deploying CV tasks on edge devices.
Benefits:
- Reduces Resource Consumption: Significantly lowers resource usage at edge devices.
- Increases Task Admission: Admits 26.9% more tasks at the edge.
- Resource Efficiency: Saves 54.7% of resources in large-scale scenarios.
- Optimized Resource Allocation: Enhances the allocation of radio and compute resources.
- Improved Latency: Reduces latency while admitting more tasks.
- Comprehensive Consideration: Takes into account memory consumption and training costs for DNNs.
- Near-Optimal Solutions: Delivers near-optimal solutions for small-scale scenarios.
- Scalability: Supports scalable offloading of CV tasks to edge devices.
- Smart Scheduling: Aids in smart task scheduling and tactical CV deployments.
- Financial Advantage: Offers financial benefits for edge server and wireless network providers.
Applications:
- Mobile Task Scheduling: Optimizes the scheduling of tasks on mobile devices.
- Fast Object Detection: Enhances the speed and efficiency of object detection.
- Smart Wireless Sensor Networks: Improves the performance of wireless sensor networks.
- Edge Device Optimization: Optimizes the functioning of edge devices.
Opportunity:
- Research collaboration
- licensing
