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I wrote a #rust crate Moden Hopfield Network. I used it to build a neural network that can be trained on the edge. See the demo linked in the README.md.

Modern Hopfield Network has much (much) larger capacity than classical Hopfield Network. They are also called Dense Associative Memory.

#holfield-network #tinyml

github.com/dilawar/moden-hopfi

GitHub - dilawar/moden-hopfield-network: Modern Hopfield Network (aka Dense Associative Memory) In RustGitHub

A few days ago I worked out a way of representing neural networks in Rust's type system via const generics and then that lead me to making a neural network library that doesn't use the standard library or any heap allocation, then that lead me to today where I have a pre-trained neural network running on an ATtiny85 approximating the output of an XOR gate, I'm in no way a very good data scientist or embedded engineer at all so the fact I managed to get a neural network running on an 1MHz 8 bit chip with 8k of program memory with 512 BYTES of ram, all whilst doing almost zero optimisations is insane to me

github.com/jasonalexander-ja/m

#rust #rustlang #tinyml #embedded

By me for @hackster_io, "Benchmarking TensorFlow and TensorFlow Lite on Raspberry Pi 5." The big take away from these new benchmarks is that the Raspberry Pi 5 has similar performance when using TensorFlow Lite to the Coral TPU, displaying essentially the same inferencing speed as Google's accelerator hardware. #ML #TinyML #AI #TensorFlow #RaspberryPi #CoralTPU hackster.io/news/benchmarking-

Benchmarking TensorFlow and TensorFlow Lite on Raspberry Pi 5Hackster.io