bwasti

Mining Bitcoin can be done with pencil, paper and the ability to communicate with others also interested in Bitcoin.

A popular post on r/wallstreetbets[1] summarizes Michael Burry’s thesis that CPI (consumer price index) is unaffected as long as inflation isn’t directly impacting consumers of the bag of goods that make up CPI. When there are fewer jobs, these consumers aren’t able to influence the prices of these goods (because they aren’t being paid inflated wages), and the metric commonly used to evaluate inflation is unchanged.

[1] https://www.reddit.com/r/wallstreetbets/comments/lr8h1v/why_...

I've posted this once before but I grabbed a domain name and preloaded a tutorial. Happy to remove if that's against the rules!

The source can be found here: https://github.com/bwasti/mebm

> It can also handle nonlinear conversions such as Fahrenheit to Celsius

huh?

You can just try the instructions with an off-the-shelf assembler. They're not supported.

I've found the M1's 128bit instructions to be quite fast. My M1 MacBook Air can hit 90GFlops on a single core. My 2019 16" MacBook Pro is only 1.5 times faster at 135GFlops per core, despite double the vector width.

Benchmark here: https://github.com/bwasti/mac_benchmark

If anyone has the new 2020 MBP with AVX512 I'll update the benchmark to include avx512 instructions! I'm very curious about those numbers

I believe problems solved by this tool generally disappear when using named tensors

perhaps title "Sweatpants Forever: How the Fashion Industry Collapsed" for context?

would a library like pybind (for C++ extensions) be compatible with the philosophy of this python implementation?

this can be done in browser with tensorflow.js + a pretrained mobilenet in a couple dozen lines of code

example: https://jott.live/html/tfjsmobilenet code: https://jott.live/code/tfjsmobilenet

Note that this a layout trick and not an algorithmic one. An algorithmic speed up that is good for dense convolutions with small kernels is to use Winograd: https://arxiv.org/abs/1509.09308 For large kernels, implementing an FFT tends to help.

Also worth keeping in mind that many modern networks use depthwise separable convolutions, which are channel wise convolutions (skipping a reduction over the channels, which is a memory bound operation) followed by 1x1 convolutions (which are exactly matrix multiplications with no im2col step).

Why not use tensorflow.js or onnx.js and just do this in the browser/locally? Models have gotten good and this type of classification is very cheap these days. Hitting the network for such a small task is very much overkill

I wrote a small piece that might provide some intuition on the usefulness of VCG auctions https://medium.com/@bwasti/mechanism-design-a8ff1b1a7bdd