Weighted randomization based on runtime data in System Verilog












0















Is there a way to do weighted randomization in System Verilog based on runtime data. Say, I have a queue of integers and a queue of weights (unsigned integers) and wish to select a random integer from the first queue as per the weights in the second queue.



int data[$] = '{10, 20, 30};

uint_t weights[$] = '{100, 200, 300};


Any random construct expects the weights hardcoded as in



constraint range { Var dist { [0:1] := 50 , [2:7] := 50 }; }


But in my case, I need to pick an element from an unknown number of elements.



PS: Assume the number of elements and weights will be the same always.






constraints system-verilog verification hdl random-seed







share|improve this question



























    0















    Is there a way to do weighted randomization in System Verilog based on runtime data. Say, I have a queue of integers and a queue of weights (unsigned integers) and wish to select a random integer from the first queue as per the weights in the second queue.



    int data[$] = '{10, 20, 30};
    
uint_t weights[$] = '{100, 200, 300};


    Any random construct expects the weights hardcoded as in



    constraint range { Var dist { [0:1] := 50 , [2:7] := 50 }; }


    But in my case, I need to pick an element from an unknown number of elements.



    PS: Assume the number of elements and weights will be the same always.






    constraints system-verilog verification hdl random-seed







    share|improve this question

























      0












      0








      0








      Is there a way to do weighted randomization in System Verilog based on runtime data. Say, I have a queue of integers and a queue of weights (unsigned integers) and wish to select a random integer from the first queue as per the weights in the second queue.



      int data[$] = '{10, 20, 30};
      
uint_t weights[$] = '{100, 200, 300};


      Any random construct expects the weights hardcoded as in



      constraint range { Var dist { [0:1] := 50 , [2:7] := 50 }; }


      But in my case, I need to pick an element from an unknown number of elements.



      PS: Assume the number of elements and weights will be the same always.






      constraints system-verilog verification hdl random-seed







      share|improve this question














      Is there a way to do weighted randomization in System Verilog based on runtime data. Say, I have a queue of integers and a queue of weights (unsigned integers) and wish to select a random integer from the first queue as per the weights in the second queue.



      int data[$] = '{10, 20, 30};
      
uint_t weights[$] = '{100, 200, 300};


      Any random construct expects the weights hardcoded as in



      constraint range { Var dist { [0:1] := 50 , [2:7] := 50 }; }


      But in my case, I need to pick an element from an unknown number of elements.



      PS: Assume the number of elements and weights will be the same always.






      constraints system-verilog verification hdl random-seed




      constraints system-verilog verification hdl random-seed






      share|improve this question













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      asked Nov 18 '18 at 6:01









      KrishnaKrishna

      6021515




      6021515
























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          Unfortunately, the dist constraint only lets you choose from a fixed number of values.



          Two approaches I can think of are




          1. Push each data value into a queue using the weight as a repetition count. In your example, you wind up with a queue of 600 values. Randomly pick an index into the queue. The selected element has the distribution you want. An example is posted here.


          2. Create an array of ranges for each weight. For your example the array would be uint_t ranges[2]'{{0,99},{100,299},{300,599}}. Then you could do the following in a constraint



            index inside {[0:weights.sum()-1]};
            foreach (data[ii])
            index inside {[ranges[ii][0]:ranges[ii][1]} -> value == date[ii];








          share|improve this answer
























          • Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

            – Krishna
            Nov 18 '18 at 16:36













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          Unfortunately, the dist constraint only lets you choose from a fixed number of values.



          Two approaches I can think of are




          1. Push each data value into a queue using the weight as a repetition count. In your example, you wind up with a queue of 600 values. Randomly pick an index into the queue. The selected element has the distribution you want. An example is posted here.


          2. Create an array of ranges for each weight. For your example the array would be uint_t ranges[2]'{{0,99},{100,299},{300,599}}. Then you could do the following in a constraint



            index inside {[0:weights.sum()-1]};
            foreach (data[ii])
            index inside {[ranges[ii][0]:ranges[ii][1]} -> value == date[ii];








          share|improve this answer
























          • Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

            – Krishna
            Nov 18 '18 at 16:36


















          1














          Unfortunately, the dist constraint only lets you choose from a fixed number of values.



          Two approaches I can think of are




          1. Push each data value into a queue using the weight as a repetition count. In your example, you wind up with a queue of 600 values. Randomly pick an index into the queue. The selected element has the distribution you want. An example is posted here.


          2. Create an array of ranges for each weight. For your example the array would be uint_t ranges[2]'{{0,99},{100,299},{300,599}}. Then you could do the following in a constraint



            index inside {[0:weights.sum()-1]};
            foreach (data[ii])
            index inside {[ranges[ii][0]:ranges[ii][1]} -> value == date[ii];








          share|improve this answer
























          • Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

            – Krishna
            Nov 18 '18 at 16:36
















          1












          1








          1







          Unfortunately, the dist constraint only lets you choose from a fixed number of values.



          Two approaches I can think of are




          1. Push each data value into a queue using the weight as a repetition count. In your example, you wind up with a queue of 600 values. Randomly pick an index into the queue. The selected element has the distribution you want. An example is posted here.


          2. Create an array of ranges for each weight. For your example the array would be uint_t ranges[2]'{{0,99},{100,299},{300,599}}. Then you could do the following in a constraint



            index inside {[0:weights.sum()-1]};
            foreach (data[ii])
            index inside {[ranges[ii][0]:ranges[ii][1]} -> value == date[ii];








          share|improve this answer













          Unfortunately, the dist constraint only lets you choose from a fixed number of values.



          Two approaches I can think of are




          1. Push each data value into a queue using the weight as a repetition count. In your example, you wind up with a queue of 600 values. Randomly pick an index into the queue. The selected element has the distribution you want. An example is posted here.


          2. Create an array of ranges for each weight. For your example the array would be uint_t ranges[2]'{{0,99},{100,299},{300,599}}. Then you could do the following in a constraint



            index inside {[0:weights.sum()-1]};
            foreach (data[ii])
            index inside {[ranges[ii][0]:ranges[ii][1]} -> value == date[ii];









          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 18 '18 at 16:29









          dave_59dave_59

          19.8k21437




          19.8k21437













          • Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

            – Krishna
            Nov 18 '18 at 16:36





















          • Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

            – Krishna
            Nov 18 '18 at 16:36



















          Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

          – Krishna
          Nov 18 '18 at 16:36







          Thank you dave_59, for confirming. I think creating such huge arrays would be time-consuming and wastage of memory, I think implementing a custom weighted random function based on $urandom/$urandom_range would be optimal. What do you think?

          – Krishna
          Nov 18 '18 at 16:36




















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