3 ms·
I mean in a practical way for a monte carlo simulation - usually in analog setup you set parameters with discretes, so you are going to need to do things like f
by buescher 5y ago
I mean in a practical way for a monte carlo simulation - usually in analog setup you set parameters with discretes, so you are going to need to do things like feeding a transconductance amplifier with your noise source. You're also going to worry about shaping and otherwise parameterizing your distribution - do you need uniformly distributed noise? gaussian noise? what are your limits?
I can imagine a lot of practical pitfalls and awkward half-solutions to trying to do an analog monte carlo; I was wondering how the OP went about it.
- pvitz 5y agoThere is actually a fascinating thesis by Yipeng Huang ("Hybrid Analog-Digital Co-Processing for Scientific Computation") that discusses toy models (i.e. Black-Scholes and variants). As usual, you have a Wiener process and thus need Gaussian noise. Yipeng Huang found that some noise stemming from a resistor ladder of the chip provided Gaussian white noise and he could control the mean by feeding it with a DAC and he also had some way to control the variance by changing some multipliers (but I can't tell you exactly how that worked). Nevertheless, this was the analog part and he faced issues with DC drift. Alternatively, he looked into generating the noise digitally with a microcontroller.
- buescher 5y agoThanks - the thesis does look interesting.