MIS
MIS
9th Edition
ISBN: 9781337681919
Author: BIDGOLI
Publisher: Cengage
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[画像:Problem 4: Optimizing Neural Architecture Search (NAS) for Resource- Constrained Environments Background: Neural Architecture Search automates the design of neural network architectures, often resulting in models that achieve high performance. However, deploying these models in resource- constrained environments leg, mobile devices, lol devices) requires optimizing them for factors like memory footprint, inference speed, and energy consumption. Task: Develop an optimized Neural Architecture Search (NAS) framework tailored for resource- constrained environments. The framework should balance model accuracy with constraints on computational resources. Your solution should include the following components: 1. Search Space Definition: . Define a search space that includes architectural elements suitable for efficiency, such as depthwise separable cawolutions, bottleneck layers, and efficient activation functions. Ensure the arch space is expressive enough to include high-performing and resource- efficient architectures 2. Evaluation Metrics: . Incorporate multi-objective optimization by defining metrics that capture both accuracy and resource constraints (e.g. number of parameters, FLOPs, latency). Explain how these metrics will guide the search proce 3. Search Strategy Choose an appropriate NAS strategy (e.g. reinforcement learning-based, evolutionary algorithms, gradient-based methods) and justify your choice. Adapt the strategy to efficiently explore the defined search space under resource constraints. 4. Resource Constraint Handling: • . Implement mechanisms to enforce or penalize resource constraints during the search (e.g. constraint-based filtering, peralty terms in the objective function). Ensure that the final architectures meet the specified resource limitations. 5. Model Evaluation and Validation: . . Describe how candidate architectures will be trained and evaluated efficiently (e.g. using proxy tasks, weight sharing). Ensure that the evaluation process is scalable and does not become a bottleneck. 6. Transferability and Generalization: Investigate how architectures found using your NAS framework transfer to different tasks or datasets within resource-constrained settings. Analyze the generalization capabilities of the optimized architectures. 7. Case Study: . Apply your NAS framework to design a model for a specific application (eg, real-time object detection on mobile devices Present the architecture discovered, its performance metrics, and how it meets the resource constraints Deliverables: . " . A comprehensive description of the NAS framework, including search space, evaluation metrics, and search strategy. Implementation details with pseudocode or code snippets for key components. Results from the case study, including performance metrics and architectural diagrams. An analysis of the trade-offs between accuracy and resource utilization in the discovered archtectures. Discussion on the transferability and potential improvements of your NAS framework.]
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Transcribed Image Text:Problem 4: Optimizing Neural Architecture Search (NAS) for Resource- Constrained Environments Background: Neural Architecture Search automates the design of neural network architectures, often resulting in models that achieve high performance. However, deploying these models in resource- constrained environments leg, mobile devices, lol devices) requires optimizing them for factors like memory footprint, inference speed, and energy consumption. Task: Develop an optimized Neural Architecture Search (NAS) framework tailored for resource- constrained environments. The framework should balance model accuracy with constraints on computational resources. Your solution should include the following components: 1. Search Space Definition: . Define a search space that includes architectural elements suitable for efficiency, such as depthwise separable cawolutions, bottleneck layers, and efficient activation functions. Ensure the arch space is expressive enough to include high-performing and resource- efficient architectures 2. Evaluation Metrics: . Incorporate multi-objective optimization by defining metrics that capture both accuracy and resource constraints (e.g. number of parameters, FLOPs, latency). Explain how these metrics will guide the search proce 3. Search Strategy Choose an appropriate NAS strategy (e.g. reinforcement learning-based, evolutionary algorithms, gradient-based methods) and justify your choice. Adapt the strategy to efficiently explore the defined search space under resource constraints. 4. Resource Constraint Handling: • . Implement mechanisms to enforce or penalize resource constraints during the search (e.g. constraint-based filtering, peralty terms in the objective function). Ensure that the final architectures meet the specified resource limitations. 5. Model Evaluation and Validation: . . Describe how candidate architectures will be trained and evaluated efficiently (e.g. using proxy tasks, weight sharing). Ensure that the evaluation process is scalable and does not become a bottleneck. 6. Transferability and Generalization: Investigate how architectures found using your NAS framework transfer to different tasks or datasets within resource-constrained settings. Analyze the generalization capabilities of the optimized architectures. 7. Case Study: . Apply your NAS framework to design a model for a specific application (eg, real-time object detection on mobile devices Present the architecture discovered, its performance metrics, and how it meets the resource constraints Deliverables: . " . A comprehensive description of the NAS framework, including search space, evaluation metrics, and search strategy. Implementation details with pseudocode or code snippets for key components. Results from the case study, including performance metrics and architectural diagrams. An analysis of the trade-offs between accuracy and resource utilization in the discovered archtectures. Discussion on the transferability and potential improvements of your NAS framework.
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