Can I read EPAM: A Predictive Energy Model for Mobile AI on EtoBox?

EPAM: A Predictive Energy Model for Mobile AI by Mallik, Anik; Wang, Haoxin; Xie, Jiang; Chen, Dawei; Han, Kyungtae is a scholarly article available to read on EtoBox.

What is EPAM: A Predictive Energy Model for Mobile AI about?

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 t

Author

Mallik, Anik; Wang, Haoxin; Xie, Jiang; Chen, Dawei; Han, Kyungtae

Published

2023

Language

EN