Apple buys edge-based AI startup Xnor.ai for a reported $200M

@techcrunch via @FuturumResearch via @ExponentialView

Techcrunch – Apple buys edge-based AI startup Xnor.ai for a reported $200M

Exponential View #254

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Futurum – Apple’s Acquisition of Xnor.ai Aims to Deliver TinyML to Edge Devices

Futurum Recommendations for Apple

Futurum recommends that Apple follow-up the Xnor.ai acquisition by acquiring a family of tools for lifecycle management of “TinyML” DevOps workflows. This will be a critically important portfolio for Apple to build as the edge AI space bursts wide open in the coming 5G era.

Apple should explore acquiring DevOps tools to support management of datasets, development of algorithms, governance of model versions, and deployment and monitoring of device-optimized edge-AI models and code.

It would behoove Apple to seek out complimentary partnerships in the TinyML ecosystem. One vendor that would be useful for Apple to explore partnering with, licensing, or acquiring outright would be Deeplite, whose first-to-market “neural architecture search” technology, was something I discussed recently in my article “CES 2020: Consumer-Facing Opportunities Accelerate Evolution of the AI DevOps Toolchain.” Already, AWS has an open-source offering, AutoGluon (more on that in the article below), that can support an equivalent automation feature for making AI neural networks smaller, faster, and more energy efficient with minimal accuracy degradation.

To further its edge-facing TinyML capabilities, Apple should also explore relationships with several other startups discussed in that recent CES post (linked above). At that event, SiFive, Inc., and CEVA, Inc. announcedthat they are partnering to deliver systems on chip for a wide range of domain-specific AI applications on edge devices for smart home, automotive, robotics, security, augmented reality, industrial, and IoT applications. Last but not least, SensiML provides a toolkit for end-to-end development of data collection, labeling, algorithm auto generation, and testing for on-device AI applications.