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About Bobby29999

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  1. Hi, thanks for your valuable feedback. As a newbie I will never take this valuable and nice forum for granted. I respect it. Please excuse my haphazard questions. Most of my questions got answered and I say thanks. And trust me, these were not homework questions. Having said that, you will see , the quality of my questions will improve next time. I can assure you.
  2. @zygot If I may ask you the same question....If my input is the string of any size and I have to use this following Hash function ( ) in order to get a integer seed as an output. Can you please tell me what should be the main design points that have to be considered w.r.t. FPGA? If I have to implement this complete Hash Function on FPGA.
  3. @[email protected] I understand what you mean and I agree. In order to make my life easier ......If I say, the starting point has to be the 32-bits Jenkins Hash ( to get the seed for the pseudo-random generator, what should be my design considerations on FPGA for the first part. The first part being 32-but Jenkins Hash to get the seed. The input to the 32-bits Jenkins can be being any arbitrary size string. @zygot No you have not hijacked. You had good points. Regarding your enlightening point, do you mean why use a pseudo-random generator (Jenkins
  4. @[email protected] Thanks for your really helpful tips. The physical reality is, the "random" value is pseudorandom, and the hash function is based on Richard Jenkin's 32-bit mix function. It takes integer inputs and the output is a random distribution.
  5. @[email protected] "Some of your question depends upon your timing requirements. How often do you need a new random number? That might determine whether you use a state machine or a deep pipeline." Very interesting point. TBH, I did not think in this dimension (timing..should have thought). Can't answer it exactly but this I know that the random value portion of the code is a slow part. The random function is based on hashing. It takes integer inputs and the output is a random distribution....but is repeatable....Thus pseudo random. If its slow, should I go for a deep pipeline? "You'll also
  6. Hi, I am working on pseudo random number generation topic. To be precise, Inverse Transform Sampling method , i.e., a method for generating sample numbers at random from any probability distribution given its cumulative distribution function. The problem that the inverse transform sampling method solves is as follows: - Let X be a random variable whose distribution can be described by the cumulative distributive function Fx. - I want to generate values of X which are distributed according to this distribution. The exact reference of what I would like to implement on FPG