EMEA tiny Talks: A Battery-Free Long-Range Wireless Smart Camera for Face Detection: An accurate…

EMEA https://www.tinyml.org/event/emea-2021
tiny Talks
A Battery-Free Long-Range Wireless Smart Camera for Face Detection: An accurate benchmark of novel Edge AI platforms and milliwatt microcontrollers
Michele MAGNO, Head of the Project-based learning Center, ETH Zurich, D-ITET

An emerging class of those devices is hosting low-power image sensors to perform surveillance, monitoring, and controlling. Miniaturized camera devices are today a commercial reality with several market products for a wide range of applications, from industrial to entertainment and autonomous navigation [1]. On the other side, those tiny camera systems are usually supplied by little energy storage, limiting their lifetime in the range of a few hours. Moreover, most of those miniaturized IoT “smart” cameras limit their intelligence or even only acquire/store the images and send them wirelessly to a smart-phone or more intelligent device, or download them off-line.
A class of ML that is becoming more and more attractive and challenging for ML is the edge ML or tiny machine learning, where ML algorithms are compressed to run in resource-constrained microcontrollers. To allow to have effective tiny ML systems, on one side, hardware specialists are designing novel hardware architectures to deal with the demand for large computational and storage capability. On the other side, software and algorithms specialists, including Google, are proposing less complex models and sophisticated training tools. However, bringing tiny ML on a resource-constrained processor is still a very challenging task due to the limited memory and computational capabilities available in low power processors. Typical processors for low power sensors are microcontrollers, the most popular ARM Cortex-M and RISC-V families, that can count of only a few hundred million operations per second (MOPS) with a power consumption of 10-100 mW, compatible with the goal of low power long-lasting intelligence devices. The recent trend is designing mW power microcontroller with parallel architecture, for instance PULP processor or the commercial version GAP8 from Greenwave or the novel architectures with hardware accelerators, such as Xmos.AI and Maxim78000 from Maxim to have more operation per clock in the same mW power envelope.
On the other hand, another big obstacle for intelligent devices, and in general for IoT devices, to become truly pervasive is their need for a long-term reliable power source. The use of batteries is the most direct way of powering wireless devices, but regular battery replacement is vital to ensure continuous operation. Such a requirement is unappealing as it implies high maintenance costs, especially in remote areas or if environmental issues related to battery disposal are of concern. Energy harvesting (EH), the technology to convert energy from environmental sources, is the most promising technology to achieve perpetually powered sensors for the IoT, with zero battery replacements over their mission lifetime. EH is already a mature technology for both commercial and residential settings. However, many challenges are still open for tiny-form factor harvesters, needed for the majority of unobtrusive smart sensors.
This work presents a battery-less video sensor node for continuous image processing and it is performing also an accurate benchmark of novel edge AI platform at parity of conditions. The proposes Tiny ML algorithm for challenging face identification with high accuracy with five faces to recognize target low power microcontroller. This work propose also a designed sensor node can run from a cold start in less than one minute from only 350 lux, thanks to the low power design and the high-efficiency energy harvesting circuit that can host both thermal and solar energy harvesters. After the cold-start, the node achieves perpetual working in the presence of the same or higher luminosity. Moreover, the node can cold start also with a very low luminosity of only 250 lux.


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