PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices

TL;DR

PreVIous is a methodology that accurately predicts CNN inference performance and energy consumption on IoT devices, aiding optimal architecture selection before deployment.

Contribution

It introduces PreVIousNet, a neural network for precise performance prediction of CNNs on low-power IoT hardware, with extensive validation across multiple models and platforms.

Findings

Prediction accuracy with 3-10% deviation from real measurements

Effective performance evaluation across seven CNN models and two embedded platforms

Potential integration with neural architecture search for optimized CNN design

Abstract

This paper presents PreVIous, a methodology to predict the performance of convolutional neural networks (CNNs) in terms of throughput and energy consumption on vision-enabled devices for the Internet of Things. CNNs typically constitute a massive computational load for such devices, which are characterized by scarce hardware resources to be shared among multiple concurrent tasks. Therefore, it is critical to select the optimal CNN architecture for a particular hardware platform according to prescribed application requirements. However, the zoo of CNN models is already vast and rapidly growing. To facilitate a suitable selection, we introduce a prediction framework that allows to evaluate the performance of CNNs prior to their actual implementation. The proposed methodology is based on PreVIousNet, a neural network specifically designed to build accurate per-layer performance predictive models. PreVIousNet incorporates the most usual parameters found in state-of-the-art network architectures. The resulting predictive models for inference time and energy have been tested against comprehensive characterizations of seven well-known CNN models running on two different software frameworks and two different embedded platforms. To the best of our knowledge, this is the most extensive study in the literature concerning CNN performance prediction on low-power low-cost devices. The average deviation between predictions and real measurements is remarkably low, ranging from 3% to 10%. This means state-of-the-art modeling accuracy. As an additional asset, the fine-grained a priori analysis provided by PreVIous could also be exploited by neural architecture search engines.