Is it possible to generate sum and carry in one line in Chisel similar to this code in Verilog?
module Adder_with_carry(
input [3:0] A,
input [3:0] B,
input Carry_in,
output [3:0] Sum,
output Carry_out
);
assign {Carry_out, Sum} = A + B + Carry_in;
endmodule
I am using this code
class Adder extends Module{
val io = new Bundle{
val a = UInt(INPUT, 3)
val b = UInt(INPUT, 3)
val carry_in = UInt (INPUT, 1)
val sum = UInt (OUTPUT, 2)
val carry_out = UInt(OUTPUT, 1)
}
val SUM = io.a + io.b + io.carry_in;
io.carry_out := SUM(2)
io.sum := SUM(1,0)
}
But I think it will be more convenient if there is a one-liner for this.
Related
I'm trying to put the last element of a list in the front of the list while keeping the rest of the elements in the same order N times. I can do it once with this function, but I want to add another parameter to the function so that the function in called N times.
Code:
fun multcshift(L, n) =
if null L then nil
else multcshift(hd(rev L)::(rev(tl(rev L))));
Thanks
To make the parameter n work, you need recursion. You need a base case at which point the function should no longer call itself, and a recursive case where it does. For this function, a good base case would be n = 0, meaning "shift the last letter in front 0 times", i.e., return L without modification.
fun multcshift(L, n) =
if n = 0
then L
else multcshift( hd(rev L)::rev(tl(rev L)) , n - 1 )
The running time of this function is terrible: For every n, reverse the list three times!
You could save at least one of those list reversals by not calling rev L twice. E.g.
fun multcshift (L, 0) = L
| multcshift (L, n) =
let val revL = rev L
in multcshift ( hd revL :: rev (tl revL) , n - 1 ) end
Those hd revL and rev (tl revL) seem like useful library functions. The process of applying a function to its own output n times seems like a good library function, too.
(* Return all the elements of a non-empty list except the last one. *)
fun init [] = raise Empty
| init ([_]) = []
| init (x::xs) = x::init xs
(* Return the last element of a non-empty list. *)
val last = List.last
(* Shift the last element of a non-empty list to the front of the list *)
fun cshift L = last L :: init L
(* Compose f with itself n times *)
fun iterate f 0 = (fn x => x)
| iterate f 1 = f
| iterate f n = f o iterate f (n-1)
fun multcshift (L, n) = iterate cshift n L
But the running time is just as terrible: For every n, call last and init once each. They're both O(|L|) just as rev.
You could overcome that complexity by carrying out multiple shifts at once. If you know you'll shift one element n times, you might as well shift n elements. Shifting n elements is equivalent to removing |L| - n elements from the front of the list and appending them at the back.
But what if you're asked to shift n elements where n > |L|? Then len - n is negative and both List.drop and List.take will fail. You could fix that by concluding that any full shift of |L| elements has no effect on the result and suffice with n (mod |L|). And what if n < 0?
fun multcshift ([], _) = raise Empty
| multcshift (L, 0) = L
| multcshift (L, n) =
let val len = List.length L
in List.drop (L, len - n mod len) #
List.take (L, len - n mod len) end
There are quite a few corner cases worth testing:
val test_zero = (multcshift ([1,2,3], 0) = [1,2,3])
val test_empty = (multcshift ([], 5); false) handle Empty => true | _ => false
val test_zero_empty = (multcshift ([], 0); false) handle Empty => true | _ => false
val test_negative = (multcshift ([1,2,3,4], ~1) = [2,3,4,1])
val test_nonempty = (multcshift ([1,2,3,4], 3) = [2,3,4,1])
val test_identity = (multcshift ([1,2,3,4], 4) = [1,2,3,4])
val test_large_n = (multcshift [1,2,3,4], 5) = [4,1,2,3])
val test_larger_n = (multcshift [1,2,3,4], 10) = [3,4,1,2])
There are 3 Uint 8 bits numbers. I want to sum up these numbers. How to describe it in chisel?
s = a + b + c // s is 10 bits number
If the only way to describe it as following, what's the benefits compare to traditional HDL?
s0 = a + b // s0 is 9 bits numebr
s1 = s0 + c // s1 is 10 bits number
I already try it in chisel, the result is not what I expect.
val in0 = Input(UInt(8.W))
val in1 = Input(UInt(8.W))
val p_out = Output(UInt(10.W))
io.p_out := io.in0 + io.in0 - io.in1
The generated RTL:
input [7:0] io_in0,
input [7:0] io_in1,
output [9:0] io_p_out
wire [8:0] _T_18;
wire [7:0] _T_19;
wire [8:0] _T_20;
wire [8:0] _T_21;
wire [7:0] _T_22;
assign io_p_out = {{2'd0}, _T_22};
assign _T_18 = io_in0 + io_in0;
assign _T_19 = _T_18[7:0]; // ??
assign _T_20 = _T_19 - io_in1;
assign _T_21 = $unsigned(_T_20); // ??
assign _T_22 = _T_21[7:0]; // ??
In order to keep the carry you should use the expanding operators +& and -&.
io.p_out := io.in0 +& io.in0 -& io.in1
https://chisel.eecs.berkeley.edu/doc/chisel-cheatsheet3.pdf
I'm completely lost on this. It was explained that functions are right justified so that let add x y = x + y;; has a function type of int -> int -> int or int -> (int -> int).
I'm not sure how I'd define a function of type (int -> int) -> int. I was thinking I'd have the first argument be a function that passes in an int and returns an int. I've tried:
let add = fun x y -> x + y --- int -> int -> int
let add = fun f x = (f x) + 3 --- ('a -> int) -> 'a -> int
What about
let eval (f: int -> int) :int = f 0
?
fun x -> (x 1) + 1;;
- : (int -> int) -> int = <fun>
or
let foo f = (f 1) + 1;;
val foo : (int -> int) -> int = <fun>
it works like
foo (fun x -> x + 1);;
- : int = 3
Your questions is highly associated with the notion of Currying.
But before that, let me say that if you want to write a function that needs a parameter to be a function, you could declare a normal function, and just use its parameter like a function. No need to complicate it. See the ex:
let f x = x(10) + 10
Now comes the currying part. In OCaml, the parameters are semantically evaluated just one at a time, and after evaluating an argument, an anonymous function is returned. This is important because it lets you supply part of the arguments of a function, creating effectively a new function (which is called Partial Application).
In the example bellow, I use + as a function (parenthesis around an operator turn it to a normal function), to create an increment function. And apply it to the previous f function.
let incr = (+) 1
f incr
The code evaluates to f incr = incr(10) + 10 = 21
This link has more information on the topic applied to OCaml.
I have a function in F# , like:
let MyFunction x =
let workingVariable1 = x + 1
let workingVariable2 = workingVariable1 + 1
let y = workingVariable2 + 1
y
Basically, MyFunction takes an input x and returns y. However, in the process of calculation, there are a few working variables (intermediate variables), and due to the nature of my work (civil engineering), I need to report all intermediate results. How should I store all working variables of the function ?
I'm not exactly sure what kind of "report" your are expecting. Is this a log of intermediate values ? How long time this log should be kept ? etc. etc This is my attempt from what I understand. It is not ideal solution because it allows to report values of intermediate steps but without knowing exactly which expression has generated the intermediate value (I think that you would like to know that a value n was an output of workingVariable1 = x + 1 expression).
So my solution is based on Computation Expressions. Computation expression are a kind of F# "monads".
First you need to define a computation expression :
type LoggingBuilder() =
let log p = printfn "intermediate result %A" p
member this.Bind(x, f) =
log x
f x
member this.Return(x) =
x
Next we create an instance of computation expression builder :
let logIntermediate = new LoggingBuilder()
Now you can rewrite your original function like this:
let loggedWorkflow x =
logIntermediate
{
let! workingVariable1 = x + 1
let! workingVariable2 = workingVariable1 + 1
let! y = workingVariable2 + 1
return y,workingVariable1,workingVariable2
}
If you run loggedWorkflow function passing in 10 you get this result :
> loggedWorkflow 10;;
intermediate result 11
intermediate result 12
intermediate result 13
val it : int * int * int = (13, 11, 12)
As I said your intermediate values are logged, however you're not sure which line of code is responsible for.
We could however enchance a little bit to get the FullName of the type with a corresponding line of code. We have to change a little our computation expression builder :
member this.Bind(x, f) =
log x (f.GetType().FullName)
f x
and a log function to :
let log p f = printfn "intermediate result %A %A" p f
If you run again loggedWorkflow function passing in 10 you get this result (this is from my script run in FSI) :
intermediate result 11 "FSI_0003+loggedWorkflow#34"
intermediate result 12 "FSI_0003+loggedWorkflow#35-1"
intermediate result 13 "FSI_0003+loggedWorkflow#36-2"
This is a hack but we get some extra information about where the expressions like workingVariable1 = x + 1 were definied (in my case it is "FSI_") and on which line of code (#34, #35-1). If your code changes and this is very likely to happen, your intermediate result if logged for a long time will be false. Note that I have not tested it outside of FSI and don't know if lines of code are included in every case.
I'm not sure if we can get an expression name (like workingVariable1 = x + 1) to log from computation expression. I think it's not possible.
Note: Instead of log function you coud define some other function that persist this intermediate steps in a durable storage or whatever.
UPDATE
I've tried to came up with a different solution and it is not very easy. However I might have hit a compromise. Let me explain. You can't get a name of value is bound to inside a computation expression. So we are not able to log for example for expression workingVariable1 = x + 1 that "'workingVariable1' result is 2". Let say we pass into our computation expression an extra name of intermediate result like that :
let loggedWorkflow x =
logIntermediate
{
let! workingVariable1 = "wk1" ## x + 1
let! workingVariable2 = "wk2" ## workingVariable1 + 1
let! y = "y" ## workingVariable2 + 1
return y,workingVariable1,workingVariable2
}
As you can see before ## sign we give the name of the intermediate result so let! workingVariable1 = "wk1" ## x + 1 line will be logged as "wk1".
We need then an extra type which would store a name and a value of the expression :
type NamedExpression<'T> = {Value:'T ; Name: string}
Then we have to redefine an infix operator ## we use une computation expression :
let (##) name v = {Value = v; Name = name}
This operator just takes left and right part of the expression and wraps it within NamedExpression<'T> type.
We're not done yet. We have to modify the Bind part of our computation expression builder :
member this.Bind(x, f) =
let {Name = n; Value = v} = x
log v n
f v
First we deconstruct the NamedExpression<'T> value into name and wraped value. We log it and apply the function f to the unwrapped value v. Log function looks like that :
let log p n = printfn "'%s' has intermediate result of : %A" n p
Now when you run the workflow loggedWorkflow 10;; you get the following result :
'wk1' has intermediate result of : 11
'wk2' has intermediate result of : 12
'y' has intermediate result of : 13
Maybe there are better way to do that, something with compiler services or so, but this is the best attempt I could do so far.
If I understand you correctly, then there are several options:
let MyFunction1 x =
let workingVariable1 = x + 1
let workingVariable2 = workingVariable1 + 1
let y = workingVariable2 + 1
y,workingVariable1,workingVariable2
MyFunction1 2 |> printfn "%A"
type OneType()=
member val Y = 0 with get,set
member val WV1 = 0 with get,set
member val WV2 = 0 with get,set
override this.ToString() =
sprintf "Y: %d; WV1: %d; WV2: %d\n" this.Y this.WV1 this.WV2
let MyFunction2 x =
let workingVariable1 = x + 1
let workingVariable2 = workingVariable1 + 1
let y = workingVariable2 + 1
new OneType(Y=y,WV1=workingVariable1,WV2=workingVariable2)
MyFunction2 2 |> printfn "%A"
Out:
(5, 3, 4)
Y: 5; WV1: 3; WV2: 4
http://ideone.com/eYNwYm
In the first function uses the tuple:
https://msdn.microsoft.com/en-us/library/dd233200.aspx
The second native data type.
https://msdn.microsoft.com/en-us/library/dd233205.aspx
It's not very "functional" way, but you can use mutable variable to store intermediate results:
let mutable workingVariable1 = 0
let mutable workingVariable2 = 0
let MyFunction x =
workingVariable1 <- x + 1
workingVariable2 <- workingVariable1 + 1
let y = workingVariable2 + 1
y
How can I return a function side-effecting lexical closure1 in Scala?
For instance, I was looking at this code sample in Go:
...
// fib returns a function that returns
// successive Fibonacci numbers.
func fib() func() int {
a, b := 0, 1
return func() int {
a, b = b, a+b
return b
}
}
...
println(f(), f(), f(), f(), f())
prints
1 2 3 5 8
And I can't figure out how to write the same in Scala.
1. Corrected after Apocalisp comment
Slightly shorter, you don't need the return.
def fib() = {
var a = 0
var b = 1
() => {
val t = a;
a = b
b = t + b
b
}
}
Gah! Mutable variables?!
val fib: Stream[Int] =
1 #:: 1 #:: (fib zip fib.tail map Function.tupled(_+_))
You can return a literal function that gets the nth fib, for example:
val fibAt: Int => Int = fib drop _ head
EDIT: Since you asked for the functional way of "getting a different value each time you call f", here's how you would do that. This uses Scalaz's State monad:
import scalaz._
import Scalaz._
def uncons[A](s: Stream[A]) = (s.tail, s.head)
val f = state(uncons[Int])
The value f is a state transition function. Given a stream, it will return its head, and "mutate" the stream on the side by taking its tail. Note that f is totally oblivious to fib. Here's a REPL session illustrating how this works:
scala> (for { _ <- f; _ <- f; _ <- f; _ <- f; x <- f } yield x)
res29: scalaz.State[scala.collection.immutable.Stream[Int],Int] = scalaz.States$$anon$1#d53513
scala> (for { _ <- f; _ <- f; _ <- f; x <- f } yield x)
res30: scalaz.State[scala.collection.immutable.Stream[Int],Int] = scalaz.States$$anon$1#1ad0ff8
scala> res29 ! fib
res31: Int = 5
scala> res30 ! fib
res32: Int = 3
Clearly, the value you get out depends on the number of times you call f. But this is all purely functional and therefore modular and composable. For example, we can pass any nonempty Stream, not just fib.
So you see, you can have effects without side-effects.
While we're sharing cool implementations of the fibonacci function that are only tangentially related to the question, here's a memoized version:
val fib: Int => BigInt = {
def fibRec(f: Int => BigInt)(n: Int): BigInt = {
if (n == 0) 1
else if (n == 1) 1
else (f(n-1) + f(n-2))
}
Memoize.Y(fibRec)
}
It uses the memoizing fixed-point combinator implemented as an answer to this question: In Scala 2.8, what type to use to store an in-memory mutable data table?
Incidentally, the implementation of the combinator suggests a slightly more explicit technique for implementing your function side-effecting lexical closure:
def fib(): () => Int = {
var a = 0
var b = 1
def f(): Int = {
val t = a;
a = b
b = t + b
b
}
f
}
Got it!! after some trial and error:
def fib() : () => Int = {
var a = 0
var b = 1
return (()=>{
val t = a;
a = b
b = t + b
b
})
}
Testing:
val f = fib()
println(f(),f(),f(),f())
1 2 3 5 8
You don't need a temp var when using a tuple:
def fib() = {
var t = (1,-1)
() => {
t = (t._1 + t._2, t._1)
t._1
}
}
But in real life you should use Apocalisp's solution.