[The Hardware Startup](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/3754/Hardware%2Bas%2Bbusiness.pdf) by Renee DiResta , Brady Forrest , and Ryan Vinyard

[Hardware-as-a-Service](https://diff.substack.com/p/hardware-as-a-service?token=eyJ1c2VyX2lkIjoxMjA3OTgxNywicG9zdF9pZCI6MTYxNTc5NzQsIl8iOiJaSkdBRyIsImlhdCI6MTYwNDA3NTgxNywiZXhwIjoxNjA0MDc5NDE3LCJpc3MiOiJwdWItMTk0MzAiLCJzdWIiOiJwb3N0LXJlYWN0aW9uIn0.J0TBzTy6xj5t47pKdE-x5uCqrW-tX4TQCLJDwVwrJ1w) by Byrne Hobart from The Diff
> Paul Graham points out something Fitbit, GoPro, and Sonos investors have also observed: hardware is hard.



## Hardware is hard

## +++ [Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards](https://arxiv.org/pdf/2007.09976.pdf) RAJEEV MURALIDHAR, The University of Melbourne and Amazon Web Services

## tinyML - [next AI revolution](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5122/Untitled%2B45.pdf)



- [deep learning with edge](https://www.cs.ucr.edu/~jiasi/pub/deep_edge_review.pdf)

> This article provides an overview of applications where deep learning is used at the
> network edge. Computer vision, natural language processing, network functions, and
> virtual and augmented reality are discussed as example application drivers

## +++ [tinyML for ubiquitous AI computing - review](https://arxiv.org/ftp/arxiv/papers/2102/2102.01255.pdf)

**the picture of the stages - Cloud ML, mobile ML, tinyML**

![cc](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5152/tinyML.PNG 'cc')