EPAM: A Predictive Energy Model for Mobile AI

• URL: http://arxiv.org/abs/2303.01509v1
• Date: Thu, 2 Mar 2023 09:11:23 GMT
• Title: EPAM: A Predictive Energy Model for Mobile AI
• Authors: Anik Mallik, Haoxin Wang, Jiang Xie, Dawei Chen, and Kyungtae Han
• Abstract summary: We introduce a comprehensive study of mobile AI applications considering different deep neural network (DNN) models and processing sources. We measure the latency, energy consumption, and memory usage of all the models using four processing sources. Our study highlights important insights, such as how mobile AI behaves in different applications (vision and non-vision) using CPU, GPU, and NNAPI.
• Score: 6.451060076703027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) has enabled a new paradigm of smart applications -- changing our way of living entirely. Many of these AI-enabled applications have very stringent latency requirements, especially for applications on mobile devices (e.g., smartphones, wearable devices, and vehicles). Hence, smaller and quantized deep neural network (DNN) models are developed for mobile devices, which provide faster and more energy-efficient computation for mobile AI applications. However, how AI models consume energy in a mobile device is still unexplored. Predicting the energy consumption of these models, along with their different applications, such as vision and non-vision, requires a thorough investigation of their behavior using various processing sources. In this paper, we introduce a comprehensive study of mobile AI applications considering different DNN models and processing sources, focusing on computational resource utilization, delay, and energy consumption. We measure the latency, energy consumption, and memory usage of all the models using four processing sources through extensive experiments. We explain the challenges in such investigations and how we propose to overcome them. Our study highlights important insights, such as how mobile AI behaves in different applications (vision and non-vision) using CPU, GPU, and NNAPI. Finally, we propose a novel Gaussian process regression-based general predictive energy model based on DNN structures, computation resources, and processors, which can predict the energy for each complete application cycle irrespective of device configuration and application. This study provides crucial facts and an energy prediction mechanism to the AI research community to help bring energy efficiency to mobile AI applications.

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