PhiNets: a scalable backbone for low-power AI at the edge

TL;DR

PhiNets is a scalable, resource-efficient deep learning backbone optimized for low-power edge devices, achieving state-of-the-art detection and tracking performance with significantly reduced parameters and power consumption.

Contribution

We introduce PhiNets, a novel scalable backbone architecture designed for resource-constrained platforms, improving efficiency and performance over existing models.

Findings

Achieves state-of-the-art detection on COCO and VOC2012

Reduces parameter count by 87-93% compared to EfficientNetv1 and MobileNetv2

Operates on a microcontroller with 10 mW power consumption

Abstract

In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity. Due to limited computational and communication capabilities, low memory and limited energy budget, bringing artificial intelligence algorithms to peripheral devices, such as the end-nodes of a sensor network, is a challenging task and requires the design of innovative methods. In this work, we present PhiNets, a new scalable backbone optimized for deep-learning-based image processing on resource-constrained platforms. PhiNets are based on inverted residual blocks specifically designed to decouple the computational cost, working memory, and parameter memory, thus exploiting all the available resources. With a YoloV2 detection head and Simple Online and Realtime Tracking, the proposed architecture has achieved the state-of-the-art results in (i) detection on the COCO and VOC2012 benchmarks, and (ii) tracking on the MOT15 benchmark. PhiNets reduce the parameter count of 87% to 93% with respect to previous state-of-the-art models (EfficientNetv1, MobileNetv2) and achieve better performance with lower computational cost. Moreover, we demonstrate our approach on a prototype node based on a STM32H743 microcontroller (MCU) with 2MB of internal Flash and 1MB of RAM and achieve power requirements in the order of 10 mW. The code for the PhiNets is publicly available on GitHub.