"Nothing is definite in this world, but it's more than likely...

"Nothing is definite in this world, but it's more than likely that AKIDA, through Prophesee, will be featured in Samsung, Sony and Motorola devices at some point - with the information we have at hand currently".

Extreme dot joining and wild speculation.

Prophesee are working with multiple partners.

Luca Verre, CEO of Prophesee himself said:

https://www.eetimes.com/neuromorphic-sensing-coming-soon-to-consumer-products/

“The ultimate goal of neuromorphic technology is to have both the sensing and processing neuromorphic or event–based, but we are not yet there in terms of maturity of this type of solution,” he said. “We are very active in this area to prepare for the future — we are working with Intel, SynSense, and other partners in this area — but in the short term, the mainstream market is occupied by conventional SoC platforms."


I also note Samsung have their own neuromorphic technology research program.

https://www.sait.samsung.co.kr/saithome/mobile/research/brain.do


Samsung have also produced their own spiking neural networks in hardware.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329666/

Always-On Sub-Microwatt Spiking Neural Network Based on Spike-Driven Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device

Abstract
This paper presents a novel spiking neural network (SNN) classifier architecture for enabling always-on artificial intelligent (AI) functions, such as keyword spotting (KWS) and visual wake-up, in ultra-low-power internet-of-things (IoT) devices. Such always-on hardware tends to dominate the power efficiency of an IoT device and therefore it is paramount to minimize its power dissipation. A key observation is that the input signal to always-on hardware is typically sparse in time. This is a great opportunity that a SNN classifier can leverage because the switching activity and the power consumption of SNN hardware can scale with spike rate. To leverage this scalability, the proposed SNN classifier architecture employs event-driven architecture, especially fine-grained clock generation and gating and fine-grained power gating, to obtain very low static power dissipation. The prototype is fabricated in 65 nm CMOS and occupies an area of 1.99 mm2. At 0.52 V supply voltage, it consumes 75 nW at no input activity and less than 300 nW at 100% input activity. It still maintains competitive inference accuracy for KWS and other always-on classification workloads. The prototype achieved a power consumption reduction of over three orders of magnitude compared to the state-of-the-art for SNN hardware and of about 2.3X compared to the state-of-the-art KWS hardware.