List of Constants in MyHDL

ListOfConstants.py

'''
Created on 14 Mar 2015

@author: Josy
'''


from __future__ import print_function
import os, random
from myhdl import *


def flattenlov(D, Q):
    ''' a simple utility to turn a list of vectors into a flattened vector '''
    # in theory, ConcatSignal() would be usable too,
    # but that constructs a new Signal, so we would still need
    # an @always_comb to assign that newly created Signal to the output Signal
    LNBR_VECTORS = len(D)
    LWIDTH_D = len(D[0])
    @always_comb
    def flattenlov():
        for i in range(LNBR_VECTORS):
            Q.next[(i+1) * LWIDTH_D : i * LWIDTH_D ] = D[i]
    return flattenlov


def simplefunc( Coeff, Clk, DA, DB, Q):
    ''' a simple operation, just for the effect ... '''

    @always_seq( Clk.posedge, reset = None)
    def calc():
        Q.next = Coeff[0] * DA + Coeff[1] * DB

    return calc


def loc( NBR_VECTORS, Clk, DA, DB, Q ):
    """
        List Of Constants
        DA, DB and Q are flattened Lists Of Vectors
    """

    # derive a few constants
    LWIDTH_D = len(DA) / NBR_VECTORS
    LWIDTH_Q = len(Q) / NBR_VECTORS
    LWIDTH_C = LWIDTH_Q - LWIDTH_D

    # must split input data into list Of Vectors
    lova = [ DA((i+1) * LWIDTH_D, i * LWIDTH_D) for i in range(NBR_VECTORS)]
    lovb = [ DB((i+1) * LWIDTH_D, i * LWIDTH_D) for i in range(NBR_VECTORS)]
    # we use a List Of Vectors to collect the results too
    result = [ Signal( intbv(0)[LWIDTH_Q:]) for _ in range(NBR_VECTORS)]

    # for testing our 'options', we use a local variable for the coefficients
    if USE_COEFF_INT:
        # this simulates, but doesn't convert
        LCOEFF = COEFF
    elif USE_COEFF_INTBV:
        # this simulates as well, but doesn't convert either
        LCOEFF = [ [intbv(COEFF[i][0])[LWIDTH_C:], intbv(COEFF[i][1])[LWIDTH_C:]] for i in range(NBR_VECTORS)]
    elif USE_COEFF_SIGNAL:
        # this simulates _and_ converts, but the conversion doesn't work ...
        LCOEFF = [ [Signal(intbv(COEFF[i][0])[LWIDTH_C:]), Signal(intbv(COEFF[i][1])[LWIDTH_C:])] for i in range(NBR_VECTORS)]

    # instantiate a
    components = []
    for i in range( NBR_VECTORS ):
        components.append( simplefunc(LCOEFF[i], Clk, lova[i], lovb[i], result[i]))

    # must flatten collected results into the output vector
    components.append( flattenlov( result, Q ))

    return  components


def tb_loc():
    ''' testing '''

    # helper routines to build/disassemble the flattened in- and out-put vectors
    def flatten( v , w):
        ''' flattens a list of integer values
                v: list
                w: width of element in bits
            assume that the values fit in the given bit-width 'w'
         '''
        r = 0
        # start with Most Significant Value (or at end of list)
        for i in range(len(v)-1,-1,-1):
            if v[i] >= 0:
                t = v[i]
            else:
                # negative so use 2's complement number
                t =  (2**w + v[i])
            # shift previous result 'w' bits left and insert the new set
            r = (r << w) + t
        return r

    def cut( v, w, n, signed = False):
        ''' cut a flattened value up into a list of integers
                v: value
                w: width of element in bits
                n: number of elements
                signed: if True: interpret the 'cut w' bits as a 2's complement representation
        '''
        r = [ 0 for _ in range(n)]
        for i in range(n):
            # mask out the 'w' lowest bits
            t = v & (2**w - 1)
            if signed:
                # test highest bit
                if t & 2**(w-1):
                    #negative, convert 2's complement number
                    r[i] = t - 2**w
                else:
                    r[i] = t
            else:
                r[i] = t
            # shift input value right by 'w' bits
            v >>= w
        return r


    # construct the test data
    if not USE_RANDOM:
        # use a regular pattern?
        tda = [[j + i for i in range(NBR_VECTORS)] for j in range(NBR_OPS)]
        tdb = [[j + i for i in range(NBR_VECTORS-1,-1,-1)] for j in range(NBR_OPS-1,-1,-1)]
    else:
        # perhaps random is better ...
        random.seed('This gives us repeatable randomness')
        tda = [[random.randrange(2**WIDTH_D) for _ in range(NBR_VECTORS)] for __ in range(NBR_OPS)]
        tdb = [[random.randrange(2**WIDTH_D) for _ in range(NBR_VECTORS-1,-1,-1)] for __ in range(NBR_OPS-1,-1,-1)]

    print( 'tda: {}'.format( tda))
    print( 'tdb: {}'.format( tdb))

    # calculate the expected result
    print ('expected result:', end = " ")
    r  = [ [0 for i in range(NBR_VECTORS)] for j in range(NBR_OPS)]
    for j in range(NBR_OPS):
        for i in range(NBR_VECTORS):
            r[j][i] = (tda[j][i] * COEFF[i][0] + tdb[j][i] * COEFF[i][1])
    print( r )

    print('received Q:     ', end = ' ')
    rq =[None for _ in range(NBR_OPS)]

    dut = loc( NBR_VECTORS, Clk, DA, DB, Q )

    tCK = 10
    @instance
    def clkgen():
        while True:
            Clk.next = 1
            yield delay( int( tCK / 2 ))
            Clk.next = 0
            yield delay( int( tCK / 2 ))

    @instance
    def stimulus():
        DA.next = 0
        DB.next = 0
        yield Clk.posedge
        yield delay(int( tCK / 4 ))
        for j in range(NBR_OPS):
            DA.next = flatten( tda[j], WIDTH_D)
            DB.next = flatten( tdb[j], WIDTH_D)
            yield Clk.posedge
            yield delay(0)
            #check the result
            rq[j] = cut(int(Q), WIDTH_Q, NBR_VECTORS)
#             print( cut(int(Q), WIDTH_Q, NBR_VECTORS), end = ' ' ) # this doesn't work?
            for i in range(NBR_VECTORS):
                if rq[j][i] != r[j][i]:
                    print( 'Failure: j {}, i {} -> received {} != expected {}'.format( j, i, rq[j][i], r[j][i]))
            yield delay(int( tCK / 4 ))
        print( rq )
        yield Clk.posedge
        raise StopSimulation

    return dut, clkgen, stimulus


def convert():
    toVHDL(loc, NBR_VECTORS, Clk, DA, DB, Q )
    toVerilog(loc, NBR_VECTORS, Clk, DA, DB, Q )


if __name__ == '__main__':
    def simulate(timesteps, mainclass):
        """Runs simulation for MyHDL Class"""
        # Remove old .vcd file, otherwise we get a list of renamed .vcd files lingering about
        filename = (mainclass.__name__ +".vcd")
        if os.access(filename,  os.F_OK):
            os.unlink(filename)

        # Run Simulation
        tb = traceSignals(mainclass)
        sim = Simulation(tb)
        sim.run(timesteps)

    NBR_OPS = 8
    NBR_VECTORS = 2
    USE_RANDOM = True
    COEFF = [ [i+1,i+2] for i in range(NBR_VECTORS)]
    # select how to describe the coefficients
    USE_COEFF_INT , USE_COEFF_INTBV , USE_COEFF_SIGNAL = [ False, False, True]

    WIDTH_D = 8
    WIDTH_C = 4 # >= log2(NBR_VECTORS+2), but keep to multiples of 4, this makes viewing hex-values in the waveform a lot easier
    WIDTH_Q = WIDTH_D + WIDTH_C

    Clk = Signal(bool(0))
    # flattened vectors as input
    DA  = Signal( intbv()[WIDTH_D * NBR_VECTORS :])
    DB  = Signal( intbv()[WIDTH_D * NBR_VECTORS :])
    # and as output
    Q   = Signal( intbv()[WIDTH_Q * NBR_VECTORS :])

    simulate( 3000 , tb_loc)
    convert()