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Quick google seach brings up https://github.com/pr701/nor_vm_core, which has a basic idea
A stack of zeros and ones can be encoded in a single number by keeping with bit-shifting and incrementing.
Pushing a 0 onto the stack is equivalent to doubling the number.
Pushing a 1 is equivalent to doubling and adding 1.
Popping is equivalent to dividing by 2, where the remainder is the number.
Rejoice is a concatenative programming language in which data is encoded as multisets that compose by multiplication. Think Fractran, without the rule-searching, or Forth without a stack.
> For example, exp(x)=eml(x,1), ln(x)=eml(1,eml(eml(1,x),1)), and likewise for all other operations I read the paper. Is there a table covering all other math operations translated to eml(x,y) form?
How would an architecture with a highly-optimized hardware implementation of EML compare with a traditional math coprocessor?
Dreadfully slow for integer math but probably some similar performance to something like a CORDIC for specific operations. If you can build an FPU that does exp() and ln() really fast, it's simple binary tree traversal to find the solution.
> eml(x,y)=exp(x)-ln(y) Exp and ln, isn't the operation its own inverse depending on the parameter? What a neat find.
This is a function from ℝ² to ℝ. It can't be its own inverse; what would that mean?
That's awesome. I always wondered if there is some way to do this.
> For example, exp(x)=eml(x,1), ln(x)=eml(1,eml(eml(1,x),1)), and likewise for all other operations I read the paper. Is there a table covering all other math operations translated to eml(x,y) form?
e^ix = cos x + i sin x
e^-ix = cos -x + i sin -x
= cos x - i sin x
e^ix + e^-ix = 2 cos x
cos x = (e*ix - e^-ix) / 2
Multiplication, division, addition and subtraction are all straightforward. So are hyperbolic trig functions. All other trig functions can be derived as per above.
So, like brainf*ck (the esoteric programming language), but for maths?
But even tighter. With eml and 1 you could encode a funtion in rpn as bits. Although you also need to encode where to put the input. The real question is what emoji to use for eml when written out.
I'm kidding, of course. You can encode anything in bits this way.
How does one actually add with this?
Don't know adding, but multiplication has diagram on the last page of the PDF. xy = eml(eml(1, eml(eml(eml(eml(1, eml(eml(1, eml(1, x)), 1)), eml(1, eml(eml(1, eml(y, 1)), 1))), 1), 1)), 1) From Table 4, I think addition is slightly more complicated?
exp(a) = eml(a, 1) ln(a)=eml(1,eml(eml(1,a),1))
Plugging those in is an excercise to the reader
I'm still reading this, but if this checks out, this is one of the most significant discoveries in years.
Why use splines or polynomials or haphazardly chosen basis functions if you can just fit (gradient descent) your data or wave functions to the proper computational EML tree?
Got a multidimensional and multivariate function to model (with random samples or a full map)? Just do gradient descent and convert it to approximant EML trees.
Perform gradient descent on EML function tree "phi" so that the derivatives in the Schroedinger equation match.
But as I said, still reading, this sounds too good to be true, but I have witnessed such things before :)