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Done ish med ov0

master
peteraa 7 jaren geleden
bovenliggende
e52f50904c
commit
840792719c
18 gewijzigde bestanden met toevoegingen van 777 en 473 verwijderingen
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  1. +2
    -1
      .gitignore
  2. +3
    -23
      TODOs.org
  3. +1
    -0
      ov0/build.sbt
  4. +0
    -46
      ov0/src/main/scala/Tile.scala
  5. +9
    -5
      ov0/src/main/scala/basics.scala
  6. +8
    -1
      ov0/src/main/scala/daisyDot.scala
  7. +10
    -13
      ov0/src/main/scala/daisyGrid.scala
  8. +27
    -33
      ov0/src/main/scala/daisyMatMul.scala
  9. +20
    -4
      ov0/src/main/scala/daisyVec.scala
  10. +74
    -41
      ov0/src/main/scala/daisyVecMat.scala
  11. +58
    -19
      ov0/src/main/scala/oppgavetekst.org
  12. +45
    -0
      ov0/src/main/scala/utils.scala
  13. +82
    -0
      ov0/src/test/scala/daisyDotTest.scala
  14. +82
    -0
      ov0/src/test/scala/daisyGridTest.scala
  15. +112
    -0
      ov0/src/test/scala/daisyMatMulTest.scala
  16. +116
    -0
      ov0/src/test/scala/daisyVecMatTest.scala
  17. +84
    -0
      ov0/src/test/scala/daisyVecTest.scala
  18. +44
    -287
      ov0/src/test/scala/tests.scala

+ 2
- 1
.gitignore Bestand weergeven

@@ -340,4 +340,5 @@ project/plugins/project/
# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
hs_err_pid*

*.fir
*.fir
*.json

+ 3
- 23
TODOs.org Bestand weergeven

@@ -22,26 +22,6 @@
** TODO Øving 1
** TODO Øving 2

* Øving 0

** Praktisk
** Kombinatorisk
*** Få absolutt enkleste krets (mux) til å fungere
*** TODO Tegn RTL for krets med to multiplexere
*** Sett sammen en krets som bruker to multiplexere
** Tilstand
*** TODO Tegn RTL for en enkel teller
*** Implementer en enkel teller
*** TODO Implementer en enkel registerAvleser
*** Tegn RTL av registeravleser
*** Implementer daisyVec
** Sette sammen
*** Lag tre daisyVecs som fylles og leses av i en test.
*** Kombiner egenlagd mux med daisyVecs
*** Kombiner til en parametriserbar matrise, med medfølgende tester.
*** Instansier to matriser
*** Multipliser dem
* Tutorials
https://github.com/ucb-bar/generator-bootcamp
https://github.com/ucb-bar/chisel-tutorial/wiki/chisel-installation

+ 1
- 0
ov0/build.sbt Bestand weergeven

@@ -50,3 +50,4 @@ scalacOptions ++= scalacOptionsVersion(scalaVersion.value)
scalacOptions ++= Seq("-language:reflectiveCalls")

javacOptions ++= javacOptionsVersion(scalaVersion.value)


+ 0
- 46
ov0/src/main/scala/Tile.scala Bestand weergeven

@@ -4,52 +4,6 @@ import chisel3.core.Input
import chisel3.iotesters.PeekPokeTester


object CoreMain {
def main(args: Array[String]): Unit = {

iotesters.Driver.execute(args, () => new mySelector(10)){
c => new mySelectorTest(c)
}
}
}

class Tile(data_width: Int, cols: Int, rows: Int) extends Module{

val io = IO(new Bundle {
val data_in = Input(UInt(data_width.W))

val reset = Input(Bool())

val data_out = Output(UInt(data_width.W))
val data_out_delayed = Output(UInt(data_width.W))
})

val data_reg = Reg(init=UInt(0, width = data_width))

io.data_out := io.data_in
data_reg := io.data_in
io.data_out_delayed := data_reg
}

class myTest(c: Tile) extends PeekPokeTester(c) {

poke(c.io.data_in, 0)
peek(c.io.data_out_delayed)
step(1)
poke(c.io.data_in, 1)
peek(c.io.data_out)
peek(c.io.data_out_delayed)
step(1)
poke(c.io.data_in, 2)
peek(c.io.data_out)
peek(c.io.data_out_delayed)
step(1)
poke(c.io.data_in, 3)
peek(c.io.data_out)
peek(c.io.data_out_delayed)

}

object Extras {
def somefun(someval: Int) : Unit = {}



+ 9
- 5
ov0/src/main/scala/basics.scala Bestand weergeven

@@ -101,8 +101,10 @@ class mySelector(numValues: Int) extends Module {
val counter = RegInit(UInt(Chisel.log2Up(numValues).W), 0.U)
val nextOutputIsFresh = RegInit(Bool(), true.B)

// Generate random values. Using the when keyword we choose which random
// value should drive the dataOut signal
/**
Generate random values. Using the when keyword we choose which random
value should drive the dataOut signal
*/
io.dataOut := 0.U
List.fill(numValues)(scala.util.Random.nextInt(100)).zipWithIndex.foreach {
case(rand, idx) =>
@@ -114,9 +116,11 @@ class mySelector(numValues: Int) extends Module {
}
}

// While chisel comes with an inbuilt Counter, we implement ours the old fashion way
// There are far more elegant ways of implementing this, read the chisel docs, discuss
// best practice among yourselves and experiment!
/**
While chisel comes with an inbuilt Counter, we implement ours the old fashion way
There are far more elegant ways of implementing this, read the chisel docs, discuss
best practice among yourselves and experiment!
*/
nextOutputIsFresh := true.B
when(io.next === true.B){
when(counter < (numValues - 1).U){


+ 8
- 1
ov0/src/main/scala/daisyDot.scala Bestand weergeven

@@ -1,7 +1,6 @@
package Core
import chisel3._
import chisel3.core.Input
import chisel3.iotesters.PeekPokeTester
import chisel3.util.Counter

/**
@@ -17,12 +16,20 @@ class daisyDot(elements: Int, dataWidth: Int) extends Module{
val outputValid = Output(Bool())
})

/**
Keep track of how many elements have been accumulated. As the interface has no
indicator that data can be invalid it should always be assumed that data IS valid.

This in turn means that the counter should tick on every cycle
*/
val counter = Counter(elements)
val accumulator = RegInit(UInt(dataWidth.W), 0.U)

/**
Your implementation here
*/
// Increment the value of the accumulator with the product of data in A and B
// When the counter reaches elements set output valid to true and flush the accumulator

/**
LF


+ 10
- 13
ov0/src/main/scala/daisyGrid.scala Bestand weergeven

@@ -2,46 +2,43 @@ package Core
import chisel3._
import chisel3.core.Input
import chisel3.iotesters.PeekPokeTester
import utilz._

/**
DaisyGrids hold n daisyVecs. Unlike the daisyVecs, daisyGrids have a select signal for selecting
which daisyVec to work on, but these daisyVecs can not be controlled from the outside.
*/
class daisyGrid(rows: Int, cols: Int, dataWidth: Int) extends Module{
class daisyGrid(dims: Dims, dataWidth: Int) extends Module{

val io = IO(new Bundle {

val readEnable = Input(Bool())
val writeEnable = Input(Bool())
val dataIn = Input(UInt(dataWidth.W))
val rowSelect = Input(UInt(8.W))

val dataOut = Output(UInt(dataWidth.W))
})

val currentRowIndex = RegInit(UInt(8.W), 0.U)
val currentColIndex = RegInit(UInt(8.W), 0.U)

val memRows = Array.fill(rows){ Module(new daisyVector(cols, dataWidth)).io }
val elements = rows*cols

val rows = Array.fill(dims.rows){ Module(new daisyVector(dims.cols, dataWidth)).io }

/**
Your implementation here
*/


/**
LF
*/
io.dataOut := 0.U

for(ii <- 0 until rows){
for(ii <- 0 until dims.rows){

memRows(ii).readEnable := 0.U
memRows(ii).dataIn := io.dataIn
rows(ii).writeEnable := 0.U
rows(ii).dataIn := io.dataIn

when(io.rowSelect === ii.U ){
memRows(ii).readEnable := io.readEnable
io.dataOut := memRows(ii).dataOut
rows(ii).writeEnable := io.writeEnable
io.dataOut := rows(ii).dataOut
}
}
}

+ 27
- 33
ov0/src/main/scala/daisyMatMul.scala Bestand weergeven

@@ -2,26 +2,30 @@ package Core
import chisel3._
import chisel3.core.Input
import chisel3.iotesters.PeekPokeTester
import utilz._

/**
The daisy multiplier creates two daisy grids, one transposed, and multiplies them.
*/
class daisyMultiplier(val rowsA: Int, val colsA: Int, val rowsB: Int, val colsB: Int, val dataWidth: Int) extends Module {
class daisyMultiplier(dims: Dims, dataWidth: Int) extends Module {

val io = IO(new Bundle {

val dataInA = Input(UInt(dataWidth.W))
val readEnableA = Input(Bool())
val writeEnableA = Input(Bool())

val dataInB = Input(UInt(dataWidth.W))
val readEnableB = Input(Bool())
val writeEnableB = Input(Bool())

val dataOut = Output(UInt(dataWidth.W))
val dataValid = Output(Bool())
val done = Output(Bool())
})

// How many cycles does it take to fill the matrices with data?

/**
Your implementation here
*/
val rowCounter = RegInit(UInt(8.W), 0.U)
val colCounter = RegInit(UInt(8.W), 0.U)

@@ -33,20 +37,25 @@ class daisyMultiplier(val rowsA: Int, val colsA: Int, val rowsB: Int, val colsB:
val resultReady = RegInit(Bool(), false.B)


////////////////////////////////////////
////////////////////////////////////////
/// We transpose matrix B. This means that if both matrices read the same input
/// stream then they will end up transposed.
val matrixA = Module(new daisyGrid(rowsA, colsA, dataWidth)).io
val matrixB = Module(new daisyGrid(colsB, rowsB, dataWidth)).io
/**
Following the same principle behind the the vector matrix multiplication, by
NOT transposing the dimensions.

When writing a multiplier for a 3x2 matrix it's implicit that this means a
3x2 matrix and 2x3, returning a 2x2 matrix. By not transposing the dimensions
we get the same effect as in VecMat
*/
val matrixA = Module(new daisyGrid(dims, dataWidth)).io
val matrixB = Module(new daisyGrid(dims, dataWidth)).io




matrixA.dataIn := io.dataInA
matrixA.readEnable := io.readEnableA
matrixA.writeEnable := io.writeEnableA

matrixB.dataIn := io.dataInB
matrixB.readEnable := io.readEnableB

printf("matrix A data in: %d\n", matrixB.dataIn)
matrixB.writeEnable := io.writeEnableB


////////////////////////////////////////
@@ -54,16 +63,16 @@ class daisyMultiplier(val rowsA: Int, val colsA: Int, val rowsB: Int, val colsB:
/// Set up counter statemachine
io.done := false.B

when(colCounter === (colsA - 1).U){
when(colCounter === (dims.cols - 1).U){
colCounter := 0.U

when(rowCounter === (rowsA - 1).U){
when(rowCounter === (dims.rows - 1).U){
rowCounter := 0.U
calculating := true.B

when(calculating === true.B){

when(rowOutputCounter === (rowsA - 1).U){
when(rowOutputCounter === (dims.rows - 1).U){
io.done := true.B
}.otherwise{
rowOutputCounter := rowOutputCounter + 1.U
@@ -99,7 +108,7 @@ class daisyMultiplier(val rowsA: Int, val colsA: Int, val rowsB: Int, val colsB:
resultReady := false.B
io.dataValid := false.B
when(calculating === true.B){
when(colCounter === (colsA - 1).U){
when(colCounter === (dims.cols - 1).U){
resultReady := true.B
}
}
@@ -117,18 +126,3 @@ class daisyMultiplier(val rowsA: Int, val colsA: Int, val rowsB: Int, val colsB:
}
io.dataOut := accumulator
}


class daisyMultiplierTest(c: daisyMultiplier) extends PeekPokeTester(c) {

poke(c.io.readEnableA, 1)
poke(c.io.readEnableB, 1)
for(ii <- 0 until 6){
println("data in:")
poke(c.io.dataInA, (ii/2) + 1)
poke(c.io.dataInB, (ii/2) + 1)
println("fill counters")
step(1)
println("////////////////////\n")
}
}

+ 20
- 4
ov0/src/main/scala/daisyVec.scala Bestand weergeven

@@ -4,21 +4,37 @@ import chisel3.core.Input
import chisel3.iotesters.PeekPokeTester

/**
DaisyVectors are not indexed. They have no control inputs or outputs, only data.
DaisyVectors are not indexed externally. They have no control inputs or outputs, only data.
*/
class daisyVector(elements: Int, dataWidth: Int) extends Module{

val io = IO(new Bundle {
val readEnable = Input(Bool())
val writeEnable = Input(Bool())
val dataIn = Input(UInt(dataWidth.W))

val dataOut = Output(UInt(dataWidth.W))
})

/**
although the vector is not accessible by index externally, an internal index is necessary
It is initialized to the value 0
*/
val currentIndex = RegInit(UInt(8.W), 0.U)

val memory = Array.fill(elements)(RegInit(UInt(dataWidth.W), 0.U))


/**
Your implementation here
*/
// Cycle the currentIndex register, it should be equal to the current (cycle % elements)

// Connect the selected output to io.dataOut
// Connect writeEnable to the selected memory (selectable with memory(currentIndex))
/**
LF
*/


when(currentIndex === (elements - 1).U ){
currentIndex := 0.U
}.otherwise{
@@ -30,7 +46,7 @@ class daisyVector(elements: Int, dataWidth: Int) extends Module{

for(ii <- 0 until elements){
when(currentIndex === ii.U){
when(io.readEnable === true.B){
when(io.writeEnable === true.B){
memory(ii) := io.dataIn
}
io.dataOut := memory(ii)


+ 74
- 41
ov0/src/main/scala/daisyVecMat.scala Bestand weergeven

@@ -5,39 +5,86 @@ import chisel3._
import chisel3.core.Input
import chisel3.iotesters.PeekPokeTester
import chisel3.util.Counter
import utilz._

/**
The daisy multiplier creates two daisy grids, one transposed, and multiplies them.
*/
class daisyVecMat(val lengthA: Int, val rowsB: Int, val colsB: Int, val dataWidth: Int) extends Module {
class daisyVecMat(matrixDims: Dims, dataWidth: Int) extends Module {

val io = IO(new Bundle {
val io = IO(
new Bundle {

val dataInA = Input(UInt(dataWidth.W))
val readEnableA = Input(Bool())
val dataInA = Input(UInt(dataWidth.W))
val writeEnableA = Input(Bool())

val dataInB = Input(UInt(dataWidth.W))
val readEnableB = Input(Bool())
val dataInB = Input(UInt(dataWidth.W))
val writeEnableB = Input(Bool())

val dataOut = Output(UInt(dataWidth.W))
val dataValid = Output(Bool())
val done = Output(Bool())
})
val dataOut = Output(UInt(dataWidth.W))
val dataValid = Output(Bool())
val done = Output(Bool())

// How many cycles does it take to fill the matrices with data?
}
)

/**
The dimensions are transposed because this is a vector * matrix multiplication

[1, 2]
[a, b, c] x [3, 4]
[5, 6]

Here the vector will output a, b, c, a, b, c, a...
The Matrix is the type you made last exercise, so it is actually just 3 more vectors
of length 2. In cycle 0 the values {1, 3, 5} may be selected, in cycle 1 {2, 4, 6}
can be selected.

However, you can make up for the impedance mismatch by transposing the matrix, storing
the data in 2 vectors of length 3 instead.

In memory matrixB will look like [1, 3, 5]
[2, 4, 6]

For a correct result, it is up to the user to input the data for matrixB in a transposed
manner. This is done in the tests, you don't need to worry about it.
*/
val dims = matrixDims.transposed

// basic linAlg
val lengthA = dims.cols

////////////////////////////////////////
////////////////////////////////////////
/// We transpose matrix B.
val vecA = Module(new daisyVector(lengthA, dataWidth)).io
val matrixB = Module(new daisyGrid(colsB, rowsB, dataWidth)).io
val matrixB = Module(new daisyGrid(dims, dataWidth)).io
val dotProductCalculator = Module(new daisyDot(lengthA, dataWidth)).io
val dataIsLoaded = RegInit(Bool(), false.B)

/**
Your implementation here
*/
// Create counters to keep track of when the matrix and vector has gotten all the data.
// You can assume that writeEnable will be synchronized with the vectors. I.e for a vector
// of length 3 writeEnable can only go from true to false and vice versa at T = 0, 3, 6, 9 etc


// Create counters to keep track of how far along the computation is.

// Set up the correct rowSelect for matrixB

// Wire up write enables for matrixB and vecA

/**
In the solution I used the following to keep track of state
You can use these if you want to, or do it however you see fit.
*/
// val currentCol = Counter(dims.cols)
// val rowSel = Counter(dims.rows)
// val aReady = RegInit(Bool(), false.B)
// val bReady = RegInit(Bool(), false.B)
// val isDone = RegInit(Bool(), false.B)
// val (inputCounterB, counterBWrapped) = Counter(io.writeEnableB, (dims.elements) - 1)
// val (numOutputted, numOutputtedWrapped) = Counter(dataValid, lengthA)
// val (inputCounterA, counterAWrapped) = Counter(io.writeEnableA, lengthA - 1)

/**
LF
@@ -49,10 +96,10 @@ class daisyVecMat(val lengthA: Int, val rowsB: Int, val colsB: Int, val dataWidt
////////////////////////////////////////
/// Wire components
vecA.dataIn := io.dataInA
vecA.readEnable := io.readEnableA
vecA.writeEnable := io.writeEnableA

matrixB.dataIn := io.dataInB
matrixB.readEnable := io.readEnableB
matrixB.writeEnable := io.writeEnableB

io.dataOut := dotProductCalculator.dataOut

@@ -66,9 +113,14 @@ class daisyVecMat(val lengthA: Int, val rowsB: Int, val colsB: Int, val dataWidt
////////////////////////////////////////
////////////////////////////////////////
/// Select the correct row
val (currentCol, colDone) = Counter(true.B, colsB)
val (rowSel, _) = Counter(colDone, rowsB)
matrixB.rowSelect := rowSel
val currentCol = Counter(dims.cols)
val rowSel = Counter(dims.rows)

when(currentCol.inc()){
rowSel.inc()
}

matrixB.rowSelect := rowSel.value


////////////////////////////////////////
@@ -77,10 +129,10 @@ class daisyVecMat(val lengthA: Int, val rowsB: Int, val colsB: Int, val dataWidt
val aReady = RegInit(Bool(), false.B)
val bReady = RegInit(Bool(), false.B)

val (inputCounterA, counterAWrapped) = Counter(io.readEnableA, lengthA - 1)
val (inputCounterA, counterAWrapped) = Counter(io.writeEnableA, lengthA - 1)
when(counterAWrapped){ aReady := true.B }

val (inputCounterB, counterBWrapped) = Counter(io.readEnableB, colsB*rowsB)
val (inputCounterB, counterBWrapped) = Counter(io.writeEnableB, (dims.elements) - 1)
when(counterBWrapped){ bReady := true.B }

dataIsLoaded := aReady & bReady
@@ -94,24 +146,5 @@ class daisyVecMat(val lengthA: Int, val rowsB: Int, val colsB: Int, val dataWidt

when(numOutputtedWrapped){ isDone := true.B }


// printf(p"dataInA = ${io.dataInA}\n")
// printf(p"validA = ${io.readEnableA}\n")
// printf(p"dataInB = ${io.dataInB}\n")
// printf(p"validB = ${io.readEnableB}\n")
// printf(p"validOut = ${io.dataValid}\n")
// printf(p"data loaded = ${dataIsLoaded}\n")
// printf(p"aReady = ${aReady}\n")
// printf(p"bReady = ${bReady}\n")

// printf(p"counter A = ${inputCounterA}\n")
// printf(p"counter B = ${inputCounterB}\n")

// printf(p"out = ${dotProductCalculator.dataOut}\n\n")




io.done := isDone

}

+ 58
- 19
ov0/src/main/scala/oppgavetekst.org Bestand weergeven

@@ -3,6 +3,9 @@
FPGAs using chisel.
In this exercise you will implement a circuit capable of performing matrix
matrix multiplication in the chisel hardware description language.
HAND IN YOUR CODE IN AN ARCHIVE WITH YOUR USERNAME (e.g peteraa_ex0).
PLEASE ENSURE THAT WHEN UNZIPPING THE TESTS CAN BE RUN.

* Your first component
There are two types of digital components: Combinatorial and stateful.
@@ -176,18 +179,25 @@
From the figure the principle of operation becomes clearer [inkskape drawing, rm sketch]
To test your implementation you can run testOnly Core.daisyVecSpec in sbt
To test your implementation you can run
sbt> testOnly Core.daisyVecSpec
in your sbt console
** Task 2 - Dot Product
Your next task is to implement daisyDot.
daisyDot should calculate the dot product of two vectors, inA and inB. Ensure that validOut
is only asserted when you have a result. Ensure that your accumulator gets flushed after
calculating your dot product.
Your next task is to implement a dot product calculator. daisyDot should
calculate the dot product of two vectors, inA and inB. Ensure that validOut
is only asserted when you have a result. Ensure that your accumulator gets
flushed after calculating your dot product.
Implement the dot product calculator in daisyDot.scala
To test your implementation you can run
sbt> testOnly Core.daisyDotSpec
in your sbt console

** Task 3 - Vector Matrix multiplication
Having implemented a dot product calculator, a vector matrix multiplier is not that different.
In imperative code we get something like this:
Having implemented a dot product calculator, a vector matrix multiplier is
not that different. In imperative code we get something like this:
#+begin_src scala
type Matrix[A] = List[List[A]]
@@ -202,22 +212,51 @@
}
#+end_src scala
This is just repeated application of dotProduct.
Since vector matrix multiplication is the dotproduct of the vector and the rows of the matrix,
This is just repeated application of dotProduct. Since vector matrix
multiplication is the dotproduct of the vector and the rows of the matrix,
the matrix must be transposed.
The skeleton code contains more hints if this did not make any sense.
*** Subtask 1 - representing a matrix
Like the dot product calculator, the first step is to implement a register bank for storing a matrix.
This can be done by creating n vectors from Task 1 and then select which row is the 'current' row.
Like the dot product calculator, the first step is to implement a register
bank for storing a matrix. This can be done by creating n vectors from Task
1 and then select which row is the 'current' row.
Implement this in daisyGrid.scala
The matrix representation you have created in this task allows you to select which row to read, but
not which column. This isn't very efficient when you want to read an entire column since you would have
to wait a full cycle for each row.
The way we deal with this is noticing that when multiplying two matrices we work on a row basis in
matrix A, and column basis on matrix B. If we simply transpose matrix B, then accessing its rows is
the same as accessing the columns of matrix B.
The matrix representation you have created in this task allows you to select
which row to read, but not which column. This isn't very efficient when you
want to read an entire column since you would have to wait a full cycle for
each row. The way we deal with this is noticing that when multiplying two
matrices we work on a row basis in matrix A, and column basis on matrix B.
If we simply transpose matrix B, then accessing its rows is the same as
accessing the columns of matrix B.
A consequence of this is that the API exposed by your matrix multiplier requires matrix B to be transposed.
A consequence of this is that the API exposed by your matrix multiplier
requires matrix B to be transposed.

*** Subtask 2 - vector matrix multiplication
You now have the necessary pieces to create a vector matrix multiplier.
Your implementation should have a vector and a matrix (grid).
Input for the vector is in order, input for the matrix is transposed.
Implement this in daisyVecMat.scala

** Task 4 - Matrix Matrix multiplication
You can now implement a matrix matrix multiplier.
You can (and should) reuse the code for this module from the vector matrix
multiplier.

Implement this in daisyMatMul.scala
When all tests are green you are good to go.

** Bonus exercise - Introspection on code quality and design choices
This "exercise" has no deliverable, but you should spend some time thinking about
what parts gave you most trouble and what you can do to change your approach.
In addition, the implementation you were railroaded into has a flaw that lead to
unescessary code duplication when going from a vector matrix multiplier to a matrix
matrix multiplier.
Why did this happen, and how could this have been avoided?

+ 45
- 0
ov0/src/main/scala/utils.scala Bestand weergeven

@@ -0,0 +1,45 @@
package Core

object utilz {

type Matrix = List[List[Int]]

def genMatrix(dims: Dims): Matrix =
List.fill(dims.rows)(
List.fill(dims.cols)(scala.util.Random.nextInt(5))
)

case class Dims(rows: Int, cols: Int){
val elements = rows*cols
def transposed = Dims(cols, rows)
}

def printVector(v: List[Int]): String =
v.mkString("[","\t","]")

def printMatrix(m: List[List[Int]]): String =
m.map(printVector).mkString("\n")

/**
Typically I'd fix the signature to Map[A,B]
Prints all the IOs of a Module
ex:

```
CycleTask[daisyVecMat](
10,
_ => println(s"at step $n"),
d => println(printModuleIO(d.peek(d.dut.io))),
)
```
*/
def printModuleIO[A,B](m: scala.collection.mutable.LinkedHashMap[A,B]): String =
m.toList.map{ case(x,y) => "" + x.toString() + " -> " + y.toString() }.reverse.mkString("\n")


def dotProduct(xs: List[Int], ys: List[Int]): Int =
(for ((x, y) <- xs zip ys) yield x * y).sum

def matrixMultiply(ma: Matrix, mb: Matrix): Matrix =
ma.map(mav => mb.transpose.map(mbv => dotProduct(mav,mbv)))
}

+ 82
- 0
ov0/src/test/scala/daisyDotTest.scala Bestand weergeven

@@ -0,0 +1,82 @@
package Core
import chisel3._
import chisel3.iotesters._
import org.scalatest.{Matchers, FlatSpec}
import testUtils._


class daisyDotSpec extends FlatSpec with Matchers {

behavior of "daisy vector"


it should "Only signal valid output at end of calculation" in {

val ins = (0 to 20).map(ii =>
CycleTask[daisyDot](
ii,
d => d.poke(d.dut.io.dataInA, 0),
d => d.poke(d.dut.io.dataInB, 0),
d => d.expect(d.dut.io.outputValid, if((ii % 3) == 2) 1 else 0),
)
)

iotesters.Driver.execute(() => new daisyDot(3, 32), new TesterOptionsManager) { c =>
IoSpec[daisyDot](ins, c).myTester
} should be(true)
}

it should "Be able to count to 3" in {

val ins = (0 to 20).map(ii =>
CycleTask[daisyDot](
ii,
d => d.poke(d.dut.io.dataInA, 1),
d => d.poke(d.dut.io.dataInB, 1),
d => d.expect(d.dut.io.outputValid, if((ii % 3) == 2) 1 else 0),
d => if(d.peek(d.dut.io.outputValid) == 1)
d.expect(d.dut.io.dataOut, 3)
)
)

iotesters.Driver.execute(() => new daisyDot(3, 32), new TesterOptionsManager) { c =>
IoSpec[daisyDot](ins, c).myTester
} should be(true)
}



it should "Be able to calculate dot products" in {

def createProblem(vecLen: Int): List[CycleTask[daisyDot]] = {

val in1 = List.fill(vecLen)(scala.util.Random.nextInt(10))
val in2 = List.fill(vecLen)(scala.util.Random.nextInt(10))

val dotProduct = (in1, in2).zipped.map(_*_).sum

(in1, in2, (0 to vecLen)).zipped.map{
case(a, b, idx) =>
CycleTask[daisyDot](
idx,
d => d.poke(d.dut.io.dataInA, a),
d => d.poke(d.dut.io.dataInB, b),
d => if(d.peek(d.dut.io.outputValid) == 1)
d.expect(d.dut.io.dataOut, dotProduct)
)
}
}


def createProblems(vecLen: Int): List[CycleTask[daisyDot]] =
List.fill(10)(createProblem(vecLen)).zipWithIndex.map{ case(probs, idx) =>
probs.map(_.delay(3*idx))
}.flatten



iotesters.Driver.execute(() => new daisyDot(3, 32), new TesterOptionsManager) { c =>
IoSpec[daisyDot](createProblems(3), c).myTester
} should be(true)
}
}

+ 82
- 0
ov0/src/test/scala/daisyGridTest.scala Bestand weergeven

@@ -0,0 +1,82 @@
package Core
import chisel3._
import chisel3.iotesters._
import org.scalatest.{Matchers, FlatSpec}
import testUtils._
import utilz._


class daisyGridSpec extends FlatSpec with Matchers {

behavior of "daisy grid"

def writeRowCheck(dims: Dims, rowSel: Int => Int): Seq[CycleTask[daisyGrid]] = {
(0 until dims.cols).map( n =>
CycleTask[daisyGrid](
n,
d => d.poke(d.dut.io.dataIn, n),
d => d.poke(d.dut.io.writeEnable, 1),
d => d.poke(d.dut.io.rowSelect, rowSel(n)))
) ++
(0 until dims.cols*2).map( n =>
CycleTask[daisyGrid](
n,
d => d.poke(d.dut.io.dataIn, 0),
d => d.poke(d.dut.io.writeEnable, 0),
d => d.poke(d.dut.io.rowSelect, rowSel(n)),
d => d.expect(d.dut.io.dataOut, n % dims.cols)).delay(dims.cols)
)
}


val dims = Dims(rows = 4, cols = 5)

it should "work like a regular daisyVec when row select is fixed to 0" in {

iotesters.Driver.execute(() => new daisyGrid(dims, 32), new TesterOptionsManager) { c =>
IoSpec[daisyGrid](writeRowCheck(dims, _ => 0), c).myTester
} should be(true)
}


it should "work like a regular daisyVec when row select is fixed to 1" in {
iotesters.Driver.execute(() => new daisyGrid(dims, 32), new TesterOptionsManager) { c =>
IoSpec[daisyGrid](writeRowCheck(dims, _ => 1), c).myTester
} should be(true)
}


it should "be able to write a matrix and output it" in {
iotesters.Driver.execute(() => new daisyGrid(dims, 32), new TesterOptionsManager) { c =>

def writeMatrix(matrix: Matrix): List[CycleTask[daisyGrid]] = {
(0 until dims.elements).toList.zipWithIndex.map{ case(n, idx) =>
val row = n / dims.cols
CycleTask[daisyGrid](
n,
d => d.poke(d.dut.io.dataIn, n),
d => d.poke(d.dut.io.writeEnable, 1),
d => d.poke(d.dut.io.rowSelect, row))
}
}

def readMatrix(matrix: Matrix): List[CycleTask[daisyGrid]] = {
(0 until dims.elements).toList.zipWithIndex.map{ case(n, idx) =>
val row = n / dims.cols
CycleTask[daisyGrid](
n,
d => d.poke(d.dut.io.dataIn, 0),
d => d.poke(d.dut.io.writeEnable, 0),
d => d.poke(d.dut.io.rowSelect, row),
d => d.expect(d.dut.io.dataOut, n))
}
}


val m = genMatrix(Dims(rows = 4, cols = 5))
val ins = writeMatrix(m) ++ readMatrix(m).map(_.delay(dims.elements))

IoSpec[daisyGrid](ins, c).myTester
} should be(true)
}
}

+ 112
- 0
ov0/src/test/scala/daisyMatMulTest.scala Bestand weergeven

@@ -0,0 +1,112 @@
package Core
import chisel3._
import chisel3.iotesters._
import org.scalatest.{Matchers, FlatSpec}
import testUtils._
import utilz._

class daisyMatMulSpec extends FlatSpec with Matchers {
def generateProblem(dims: Dims): List[CycleTask[daisyMultiplier]] = {

val matrixA = genMatrix(dims)
val matrixB = genMatrix(dims).transpose

val answers = matrixMultiply(matrixA, matrixB)

println("Multiplying matrix A")
println(printMatrix(matrixA))
println("with matrix B")
println(printMatrix(matrixB))
println("The input order of matrix B is")
println(printMatrix(matrixB.transpose))
println("Expected output is")
println(printMatrix(answers))


val matrixInputA = matrixA.flatten.zipWithIndex.map{
case(in, idx) =>
CycleTask[daisyMultiplier](
idx,
d => d.poke(d.dut.io.dataInA, in),
d => d.poke(d.dut.io.writeEnableA, 1)
)
}


val matrixInputB = matrixB.transpose.flatten.zipWithIndex.map{
case(in, idx) =>
CycleTask[daisyMultiplier](
idx,
d => d.poke(d.dut.io.dataInB, in),
d => d.poke(d.dut.io.writeEnableB, 1)
)
}


val disableInputs = List(
CycleTask[daisyMultiplier](
dims.elements,
d => d.poke(d.dut.io.writeEnableA, 0)
),
CycleTask[daisyMultiplier](
dims.elements,
d => d.poke(d.dut.io.writeEnableB, 0)
)
)


val checkValid1 = (0 until dims.elements).map( n =>
CycleTask[daisyMultiplier](
n,
d => d.expect(d.dut.io.dataValid, 0, "data valid should not be asserted before data is ready")
)
).toList



val checkValid2 = (0 until dims.rows * dims.rows * dims.cols).map{ n =>

val shouldBeValid = (n % dims.cols) == dims.cols - 1

val answerRowIndex = n/(dims.rows*dims.cols)
val answerColIndex = ((n-1)/(dims.cols)) % dims.rows

val expectedOutput = answers(answerRowIndex)(answerColIndex)

CycleTask[daisyMultiplier](
n,
d => if(!shouldBeValid)
d.expect(d.dut.io.dataValid, 0)
else {
d.expect(d.dut.io.dataValid, 1)
d.expect(d.dut.io.dataOut, expectedOutput)
}

).delay(dims.elements + 1)
}.toList


// adds a lot of annoying noise
// val peekDebug = (0 until 20).map(n =>
// CycleTask[daisyMultiplier](
// n,
// _ => println(s"at step $n"),
// d => println(printModuleIO(d.peek(d.dut.io))),
// _ => println(),
// )
// ).toList

matrixInputA ::: matrixInputB ::: disableInputs ::: checkValid1 ::: checkValid2 // ::: peekDebug
}


behavior of "mat multiplier"

val dims = Dims(rows = 3, cols = 2)

it should "work" in {
iotesters.Driver.execute(() => new daisyMultiplier(dims, 32), new TesterOptionsManager) { c =>
IoSpec[daisyMultiplier](generateProblem(Dims(rows = 3, cols = 2)), c).myTester
} should be(true)
}
}

+ 116
- 0
ov0/src/test/scala/daisyVecMatTest.scala Bestand weergeven

@@ -0,0 +1,116 @@
package Core
import chisel3._
import chisel3.iotesters._
import org.scalatest.{Matchers, FlatSpec}
import testUtils._
import utilz._


class daisyVecMatSpec extends FlatSpec with Matchers {

def generateProblem(dims: Dims): List[CycleTask[daisyVecMat]] = {

// for a vec len A, a matrix must have dims A rows
val matrixB = genMatrix(dims).transpose
val vecA = List.fill(dims.rows)(scala.util.Random.nextInt(5))

def answers: List[Int] = matrixB.map( col =>
(col, vecA).zipped.map(_*_).sum)

println("multiplying vector: ")
println(printVector(vecA))
println("with matrix:")
println(printMatrix(matrixB.transpose))
println("which should equal")
println(printVector(answers))

println("Input order of matrix:")
println(printMatrix(matrixB))


val vecInput = vecA.zipWithIndex.map{
case(in, idx) =>
CycleTask[daisyVecMat](
idx,
d => d.poke(d.dut.io.dataInA, in),
d => d.poke(d.dut.io.writeEnableA, 1)
)
}


val matrixInput = matrixB.flatten.zipWithIndex.map{
case(in, idx) =>
CycleTask[daisyVecMat](
idx,
d => d.poke(d.dut.io.dataInB, in),
d => d.poke(d.dut.io.writeEnableB, 1)
)
}


val inputDisablers = List(
CycleTask[daisyVecMat](
dims.rows,
d => d.poke(d.dut.io.writeEnableA, 0)
),
CycleTask[daisyVecMat](
dims.elements,
d => d.poke(d.dut.io.writeEnableB, 0)
)
)


val checkValid1 = (0 until dims.elements).map( n =>
CycleTask[daisyVecMat](
n,
d => d.expect(d.dut.io.dataValid, 0, "data valid should not be asserted before data is ready")
)
).toList




val checkValid2 = (0 until dims.elements).map{ n =>

val shouldBeValid = (n % dims.rows) == dims.rows - 1

val whichOutput = answers( (n/dims.rows) )

CycleTask[daisyVecMat](
n,
d => if(!shouldBeValid)
d.expect(d.dut.io.dataValid, 0)
else {
d.expect(d.dut.io.dataValid, 1)
d.expect(d.dut.io.dataOut, whichOutput)
}

).delay(dims.elements)
}.toList


// adds a lot of annoying noise
// val peekDebug = (0 until 20).map(n =>
// CycleTask[daisyVecMat](
// n,
// _ => println(s"at step $n"),
// d => println(printModuleIO(d.peek(d.dut.io))),
// _ => println(),
// )
// ).toList

vecInput ::: matrixInput ::: inputDisablers ::: checkValid1 ::: checkValid2 // ::: peekDebug
}



behavior of "vec mat multiplier"

val dims = Dims(rows = 3, cols = 2)

it should "work" in {
iotesters.Driver.execute(() => new daisyVecMat(dims, 32), new TesterOptionsManager) { c =>
IoSpec[daisyVecMat](generateProblem(Dims(rows = 3, cols = 2)), c).myTester
} should be(true)
}
}

+ 84
- 0
ov0/src/test/scala/daisyVecTest.scala Bestand weergeven

@@ -0,0 +1,84 @@
package Core
import chisel3._
import chisel3.iotesters._
import org.scalatest.{Matchers, FlatSpec}
import testUtils._


class daisyVecSpec extends FlatSpec with Matchers {

behavior of "daisy vector"

it should "not write when write enable is low" in {

val ins = (0 to 10).map(ii =>
CycleTask[daisyVector](
ii,
d => d.poke(d.dut.io.dataIn, 0),
d => d.poke(d.dut.io.writeEnable, 0),
d => d.expect(d.dut.io.dataOut, 0))
).toList


iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>
IoSpec[daisyVector](ins, c).myTester
} should be(true)
}



it should "write only when write enable is asserted" in {

val ins =
(0 until 4).map(ii =>
CycleTask[daisyVector](
ii,
_ => println("inputting 2s'"),
d => d.poke(d.dut.io.dataIn, 2),
d => d.poke(d.dut.io.writeEnable, 1))) ++
(0 until 6).map(ii =>
CycleTask[daisyVector](
ii + 4,
_ => println("Checking output is 2"),
d => d.poke(d.dut.io.writeEnable, 0),
d => d.expect(d.dut.io.dataOut, 2)
))

iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>
IoSpec[daisyVector](ins, c).myTester
} should be(true)
}



it should "Work in general" in {


val ins = {
val inputs = List.fill(10)(scala.util.Random.nextInt(10000))

println(inputs)

val in = inputs.zipWithIndex.map{ case(in,idx) =>
CycleTask[daisyVector](
idx,
d => d.poke(d.dut.io.dataIn, in),
d => d.poke(d.dut.io.writeEnable, 1)
)
}

val out = inputs.zipWithIndex.map{ case(expected, idx) =>
CycleTask[daisyVector](
idx + 4,
d => d.expect(d.dut.io.dataOut, expected)
)
}

in ::: out
}

iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>
IoSpec[daisyVector](ins, c).myTester
} should be(true)
}
}

+ 44
- 287
ov0/src/test/scala/tests.scala Bestand weergeven

@@ -1,317 +1,74 @@
package Core
import chisel3._
import chisel3.util._
import chisel3.core.Input
import chisel3.iotesters._
import org.scalatest.{Matchers, FlatSpec}


class daisyVectorTest(c: daisyVector, inputs: List[(Int, Int, Int)]) extends PeekPokeTester(c) {

(inputs).foreach {
case(enIn, dataIn, dataOut) => {
poke(c.io.readEnable, enIn)
poke(c.io.dataIn, dataIn)
expect(c.io.dataOut, dataOut)
step(1)
}
}

}

class daisyDotTest(c: daisyDot, inputs: List[(Int, Int, Option[Int], Int)]) extends PeekPokeTester(c) {

(inputs).foreach {
case(inA, inB, dataOut, dataValid) => {
poke(c.io.dataInA, inA)
poke(c.io.dataInB, inB)
dataOut.foreach { expect(c.io.dataOut, _) }
expect(c.io.outputValid, dataValid)
step(1)
}
}

}

class daisyGridTest(c: daisyGrid, inputs: List[(Int, Int, Int, Int)]) extends PeekPokeTester(c) {

(inputs).foreach {
case(readEnable, dataIn, readRow, dataOut) => {
poke(c.io.readEnable, readEnable)
poke(c.io.dataIn, dataIn)
poke(c.io.rowSelect, readRow)
expect(c.io.dataOut, dataOut)
step(1)
}
}

}


class daisyVecMatTest(c: daisyVecMat, inputs: List[(Int,Int,Int,Int,Option[Int],Int,Int)]) extends PeekPokeTester(c) {

(inputs).foreach {
case(dataInA, readEnableA, dataInB, readEnableB, dataOutExpect, dataValidExpect, doneExpect) => {
poke(c.io.dataInA, dataInA)
poke(c.io.dataInB, dataInB)
poke(c.io.readEnableA, readEnableA)
poke(c.io.readEnableB, readEnableB)
expect(c.io.dataValid, dataValidExpect)
expect(c.io.done, doneExpect)
dataOutExpect.foreach { expect(c.io.dataOut, _) }
step(1)
}
}

}


class daisyVecSpec extends FlatSpec with Matchers {

val input1 = List.fill(10)((0, 0x45, 0))

val input2 = input1 ++ List(
// enableIn, dataIn, expected
(1, 2, 0),
(1, 2, 0),
(1, 2, 0),
(1, 2, 0),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2),
(0, 0, 2))
object testUtils {

/**
Somewhat unintuitively named, a cycle task is a list test tasks at some time step.
In order to not have to supply a list the scala varargs syntax (*) is used.
As an example, at step 13 we want to input a value to a signal in: (PeekPokeTester[T] => Unit)
and check an output out: ((PeekPokeTester[T] => Unit) with the possibility of test failure exception)
Thanks to varargs syntax this would be
CycleTask[MyModule](13, in, out)

val input3 = {
val inputs = List.fill(100)(scala.util.Random.nextInt(10000))
val withExpected = (List.fill(4)(0) ++ inputs) zip inputs
val withEnabled = withExpected.map{ case(expected, in) => (1, in, expected) }
Sometimes it is convenient to delay a bunch of checks by some set amount of cycles.
For instance, assume a component needs 10 cycles to set up, but it's more convenient
to write tests from T = 0, we do that and then call .delay(10) to ensure the T0 for the
tasks is actually T = 10
*/
case class CycleTask[T <: Module](step: Int, run: PeekPokeTester[T] => Unit*){

withEnabled
}

behavior of "daisy vector"

it should "not read when read enable is low" in {
iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>
new daisyVectorTest(c, input1)
} should be(true)
}


it should "read only when read enable is asserted" in {
iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>
new daisyVectorTest(c, input2)
} should be(true)
}


it should "Work in general" in {
iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>
new daisyVectorTest(c, input3)
} should be(true)
}
}


class daisyDotSpec extends FlatSpec with Matchers {
behavior of "daisy vector"

val input1 = List(
(0, 0, None, 0),
(0, 0, None, 0),
(0, 0, None, 1),
(0, 0, None, 0),
(0, 0, None, 0),
(0, 0, None, 1),
(0, 0, None, 0),
(0, 0, None, 0),
(0, 0, None, 1))

it should "Only signal valid output at end of calculation" in {
iotesters.Driver.execute(() => new daisyDot(3, 32), new TesterOptionsManager) { c =>
new daisyDotTest(c, input1)
} should be(true)
}


val input2 = List(
(1, 0, None, 0),
(1, 0, None, 0),
(1, 0, Some(3), 1),
(1, 0, None, 0),
(1, 0, None, 0),
(1, 0, Some(3), 1),
(1, 0, None, 0),
(1, 0, None, 0),
(1, 0, Some(3), 1))

it should "Be able to count to 3" in {
iotesters.Driver.execute(() => new daisyDot(3, 32), new TesterOptionsManager) { c =>
new daisyDotTest(c, input1)
} should be(true)
// :_* is necessary for calling var args with explicit list
def delay(by: Int) = CycleTask[T](step + by, run:_*)
}

def createProblem(vecLen: Int): List[(Int, Int, Option[Int], Int)] = {
val in1 = List.fill(vecLen)(scala.util.Random.nextInt(10))
val in2 = List.fill(vecLen)(scala.util.Random.nextInt(10))

val dotProduct = (in1, in2).zipped.map(_*_).sum
/**
Takes in a list of cycle tasks, sorts them by timestep to execute and runs until all cycletasks are done
*/
case class IoSpec[T <: Module](
instructions: Seq[CycleTask[T]],
component: T
){
val lastStep = instructions.maxBy(_.step).step
val instructionsMap = instructions.groupBy(_.step)

(in1, in2, (0 to vecLen)).zipped.map{
case(a, b, idx) =>
val dpExpect = if(idx == (vecLen - 1)) Some(dotProduct) else None
val outExpect = if(idx == (vecLen - 1)) 1 else 0

(a, b, dpExpect, outExpect)
}
}


def createProblems(vecLen: Int): List[(Int, Int, Option[Int], Int)] =
List.fill(10)(createProblem(vecLen)).flatten


it should "Be able to calculate dot products" in {
iotesters.Driver.execute(() => new daisyDot(10, 32), new TesterOptionsManager) { c =>
new daisyDotTest(c, createProblems(10))
} should be(true)
}
}


class daisyGridSpec extends FlatSpec with Matchers {
type Matrix[A] = List[List[A]]

behavior of "daisy grid"

def genMatrix(dims: (Int,Int)): Matrix[Int] =
List.fill(dims._1)(
List.fill(dims._2)(scala.util.Random.nextInt(100))
)

def readRowCheck(dims: (Int,Int)): List[(Int,Int,Int,Int)] = {
// readEn, dataIn, readRow, expected dataOut
List.fill(dims._1 - 1)(( 1, 1, 0, 0)) ++
List.fill(dims._1 - 1)((0, 0, 0, 1)) ++
List.fill(dims._1 - 1)((0, 0, 0, 1))
}

def readRow2Check(dims: (Int,Int)): List[(Int,Int,Int,Int)] = {
// readEn, dataIn, readRow, expected dataOut
List.fill(dims._1 - 1)(( 1, 1, 1, 0)) ++
List.fill(dims._1 - 1)((0, 0, 1, 1)) ++
List.fill(dims._1 - 1)((0, 0, 1, 1))
}

def readMatrix(dims: (Int,Int)): List[(Int,Int,Int,Int)] = {
val m = genMatrix(dims)
val input = m.zipWithIndex.map{ case(row, rowIdx) =>
row.zipWithIndex.map{ case(a, colIdx) =>
// readEn, dataIn, readRow, expected dataOut
( 1, a, rowIdx, 0)
class tester(c: T) extends PeekPokeTester(c)
val myTester: PeekPokeTester[T] = new tester(component) {
for(ii <- 0 to lastStep){
instructionsMap.getOrElse(ii, Nil).foreach(_.run.foreach(t => t(this)))
step(1)
}
}.flatten

val output = m.zipWithIndex.map{ case(row, rowIdx) =>
row.zipWithIndex.map{ case(a, colIdx) =>
// readEn, dataIn, readRow, expected dataOut
( 0, 0, rowIdx, a)
}
}.flatten

input ++ output
}


it should "work like a regular daisyVec when row select is fixed to 0" in {
iotesters.Driver.execute(() => new daisyGrid(5, 4, 32), new TesterOptionsManager) { c =>
new daisyGridTest(c, readRowCheck((5,4)))
} should be(true)
}


it should "work like a regular daisyVec when row select is fixed to 1" in {
iotesters.Driver.execute(() => new daisyGrid(5, 4, 32), new TesterOptionsManager) { c =>
new daisyGridTest(c, readRow2Check((5,4)))
} should be(true)
}
}


it should "be able to read a matrix" in {
iotesters.Driver.execute(() => new daisyGrid(5, 4, 32), new TesterOptionsManager) { c =>
new daisyGridTest(c, readMatrix((5,4)))
} should be(true)
}
}

class testUtilSpec extends FlatSpec with Matchers {
import testUtils._

class daisyVecMatSpec extends FlatSpec with Matchers {
type Matrix[A] = List[List[A]]
def genMatrix(dims: (Int,Int)): Matrix[Int] =
List.fill(dims._1)(
List.fill(dims._2)(scala.util.Random.nextInt(4))
)


def generateInputs(dims: (Int,Int)): List[(Int,Int,Int,Int,Option[Int],Int,Int)] = {

val matrixB = genMatrix(dims)
val vecA = genMatrix((1, (dims._1))).head

println("multiplying: ")
println(vecA.mkString("[","\t","]"))
println("matrix:")
matrixB.foreach { row =>
println(row.mkString("[","\t","]"))
}

def answers: List[Int] = matrixB.transpose.map( col =>
(col, vecA).zipped.map(_*_).sum
val ins = List[CycleTask[daisyVector]](
CycleTask(
1,
d => d.poke(d.dut.io.dataIn, 1),
d => d.expect(d.dut.io.dataOut, 0, s"fail at step ${d.t}")
)
)

println("should equal")
println(answers.mkString("[","\t","]"))


val vecAndMatrixInput = (matrixB.head zip vecA).map{
case(m, v) =>
(v, 1, m, 1, None, 0, 0)
}

val matrixInput = matrixB.tail.flatten.map{ m =>
(0, 0, m, 1, None, 0, 0)
}

val checkOutput = answers.map( a =>
{
val filler = List.fill(dims._2 - 1)((0, 0, 0, 0, None, 0, 0))
val check = List((0, 0, 0, 0, Some(a), 0, 0))
filler ++ check
}).flatten

vecAndMatrixInput ::: matrixInput ::: checkOutput
}

behavior of "my simple test harness attempt"
it should "not NPE" in {
iotesters.Driver.execute(() => new daisyVector(4, 32), new TesterOptionsManager) { c =>

val myTest = IoSpec[daisyVector](ins, c)

behavior of "vec mat multiplier"
myTest.myTester

it should "compile" in {
iotesters.Driver.execute(() => new daisyVecMat(5, 4, 5, 32), new TesterOptionsManager) { c =>
new daisyVecMatTest(c, Nil)
} should be(true)
}



it should "Not assert valid output when loading data" in {
iotesters.Driver.execute(() => new daisyVecMat(5, 4, 5, 32), new TesterOptionsManager) { c =>
new daisyVecMatTest(c, generateInputs((5,4)))
} should be(true)
}
}

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