Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems
Current embedded systems are specifically designed to run multimedia applications. These applications have a big impact on both performance and energy consumption. Both metrics can be optimized selecting the best cache configuration for a target set of applications. Multi-objective optimization may help to minimize both conflicting metrics in an independent manner. In this work, we propose an optimization method that based on Multi-Objective Evolutionary Algorithms, is able to find the best cache configuration for a given set of applications. To evaluate the goodness of candidate solutions, the execution of the optimization algorithm is combined with a static profiling methodology using several well-known simulation tools. Results show that our optimization framework is able to obtain an optimized cache for Mediabench applications. Compared to a baseline cache memory, our design method reaches an average improvement of 64.43\% and 91.69\% in execution time and energy consumption, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Evolutionary AlgorithmsSimilar Papers 제목 키워드 기반
Performance and Energy-Aware Bi-objective Tasks Scheduling for Cloud Data Centers
Cloud computing enables remote execution of users tasks. The pervasive adoption of cloud computing in smart cities services and applications requires timely execution of tasks adhering to Quality of Services (QoS). Howev…
Cloud ComputingSchedulingDynamic GPU Energy Optimization for Machine Learning Training Workloads
GPUs are widely used to accelerate the training of machine learning workloads. As modern machine learning models become increasingly larger, they require a longer time to train, leading to higher GPU energy consumption. …
BIG-bench Machine LearningGPUSchedulingKareus: Joint Reduction of Dynamic and Static Energy in Large Model Training
The computing demand of AI is growing at an unprecedented rate, but energy supply is not keeping pace. As a result, energy has become an expensive and contended resource that requires explicit management and optimization…
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks
Embedded deep learning platforms have witnessed two simultaneous improvements. First, the accuracy of convolutional neural networks (CNNs) has been significantly improved through the use of automated neural-architecture …
General Classificationimage-classificationImage ClassificationNeural Architecture SearchEnergy consumption of code small language models serving with runtime engines and execution providers
Background. The rapid growth of Language Models (LMs), particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impact. Optimizing LMs inf…
Code GenerationCPU