updated synth plotting and regression

pipelined/src/ppa/ppa.sv

@@ -313,6 +313,7 @@ module ppa_shifter #(parameter WIDTH=32) (
   assign Y = zshift[WIDTH-1:0];    
 endmodule
 
+// just report one hot
 module ppa_prioritythermometer #(parameter N = 8) (
   input  logic  [N-1:0] a,
   output logic  [N-1:0] y);
@@ -338,7 +339,7 @@ module ppa_priorityonehot #(parameter N = 8) (
   assign y = a & nolower;
 endmodule
 
-module ppa_prioriyencoder #(parameter N = 8) (
+module ppa_priorityencoder #(parameter N = 8) (
   input  logic  [N-1:0] a,
   output logic  [$clog2(N)-1:0] y);
   // Carefully crafted so design compiler will synthesize into a fast tree structure

synthDC/ppaAnalyze.py

@@ -1,6 +1,8 @@
 #!/usr/bin/python3
 from distutils.log import error
+from statistics import median
 import subprocess
+import statistics
 import csv
 import re
 import matplotlib.pyplot as plt
@@ -32,13 +34,17 @@ def getData():
     for i in range(len(linesCPL)):
         line = linesCPL[i]
         mwm = wm.findall(line)[0][4:-4].split('_')
+        freq = int(f.findall(line)[0][1:-4])
+        delay = float(cpl.findall(line)[0])
+        area = float(da.findall(linesDA[i])[0])
+        mod = mwm[0]
+        width = int(mwm[1])
+
         power = p.findall(linesP[i])
-        oneSynth = [mwm[0], int(mwm[1])]
-        oneSynth += [int(f.findall(line)[0][1:-4])]
-        oneSynth += [float(cpl.findall(line)[0])]
-        oneSynth += [float(da.findall(linesDA[i])[0])]
-        oneSynth += [float(power[1])]
-        oneSynth += [float(power[2])]
+        lpower = float(power[2])
+        denergy = float(power[1])/freq
+
+        oneSynth = [mod, width, freq, delay, area, lpower, denergy]
         allSynths += [oneSynth]
 
     return allSynths
@@ -47,133 +53,209 @@ def getVals(module, freq, var):
     global allSynths
     if (var == 'delay'):
         ind = 3 
-        units = " (ps)"
+        units = " (ns)"
     elif (var == 'area'):
         ind = 4
         units = " (sq microns)"
-    elif (var == 'dpower'):
-        ind = 5
-        units = " (mW)"
     elif (var == 'lpower'):
-        ind = 6
+        ind = 5
         units = " (nW)"
+    elif (var == 'denergy'):
+        ind = 6
+        units = " (uJ)" #fix check math
     else:
         error
 
     widths = []
-    ivar = []
+    metric = []
     for oneSynth in allSynths:
         if (oneSynth[0] == module) & (oneSynth[2] == freq):
             widths += [oneSynth[1]]
-            ivar += [oneSynth[ind]]
-    return widths, ivar, units
+            m = oneSynth[ind]
+            if (ind==6): m*=1000
+            metric += [m]
+    return widths, metric, units
 
 def writeCSV(allSynths):
     file = open("ppaData.csv", "w")
     writer = csv.writer(file)
-    writer.writerow(['Module', 'Width', 'Target Freq', 'Delay', 'Area', 'D Power (mW)', 'L Power (nW)'])
+    writer.writerow(['Module', 'Width', 'Target Freq', 'Delay', 'Area', 'L Power (nW)', 'D energy (mJ)'])
 
     for one in allSynths:
         writer.writerow(one)
 
     file.close()
 
-def polyfitR2(x, y, deg):
-    ''' from internet, check math'''
-    z = np.polyfit(x, y, deg)
-    p = np.poly1d(z)
-    yhat = p(x)                         # or [p(z) for z in x]    
-    ybar = np.sum(y)/len(y)          # or sum(y)/len(y)    
-    ssreg = np.sum((yhat-ybar)**2)   # or sum([ (yihat - ybar)**2 for yihat in yhat])    
-    sstot = np.sum((y - ybar)**2)    # or sum([ (yi - ybar)**2 for yi in y])    
-    r2 = ssreg / sstot    
-    return p, r2
+def genLegend(fits, coefs, module, r2):
 
-def plotPPA(module, freq, var):
+    coefsr = [str(round(c, 3)) for c in coefs]
+
+    eq = ''
+    ind = 0
+    if 'c' in fits:
+        eq += coefsr[ind]
+        ind += 1
+    if 'l' in fits:
+        eq += " + " + coefsr[ind] + "*N"
+        ind += 1
+    if 's' in fits:
+        eq += " + " + coefsr[ind] + "*N^2"
+        ind += 1
+    if 'g' in fits:
+        eq += " + " + coefsr[ind] + "*log2(N)"
+        ind += 1
+    if 'n' in fits:
+        eq += " + " + coefsr[ind] + "*Nlog2(N)"
+        ind += 1
+
+    legend_elements = [lines.Line2D([0], [0], color='orange', label=eq),
+                       lines.Line2D([0], [0], color='steelblue', ls='', marker='o', label=' R^2='+ str(round(r2, 4)))]
+    return legend_elements
+
+def plotPPA(module, freq, var, ax=None, fits='clsgn'):
     '''
     module: string module name
-    freq: int freq (GHz)
-    var: string 'delay' or 'area'
+    freq: int freq (MHz)
+    var: string delay, area, lpower, or denergy
+    fits: constant, linear, square, log2, Nlog2
     plots chosen variable vs width for all matching syntheses with regression
     '''
-
-    # A = np.vstack([x, np.ones(len(x))]).T
-    # mcresid = np.linalg.lstsq(A, y, rcond=None)
-    # m, c = mcresid[0]
-    # resid = mcresid[1]
-    # r2 = 1 - resid / (y.size * y.var())
-    # p, r2p = polyfitR2(x, y, 2)
-    # zlog = np.polyfit(np.log(x), y, 1)
-    # plog = np.poly1d(zlog)
-    # xplog = np.log(xp)
-    # _ = plt.plot(x, m*x + c, 'r', label='Linear fit R^2='+ str(r2)[1:7])
-    # _ = plt.plot(xp, p(xp), label='Quadratic fit R^2='+ str(r2p)[:6])
-    # _ = plt.plot(xp, plog(xplog), label = 'Log fit')
-
-    widths, ivar, units = getVals(module, freq, var)
-    coefs, r2 = regress(widths, ivar)
+    widths, metric, units = getVals(module, freq, var)
+    coefs, r2, funcArr = regress(widths, metric, fits)
 
     xp = np.linspace(8, 140, 200)
-    pred = [coefs[0] + x*coefs[1] + np.log(x)*coefs[2] + x*np.log(x)*coefs[3] for x in xp]
+    pred = []
+    for x in xp:
+        y = [func(x) for func in funcArr]
+        pred += [sum(np.multiply(coefs, y))]
 
-    r2p = round(r2[0], 4)
-    rcoefs = [round(c, 3) for c in coefs]
+    if ax is None:
+        singlePlot = True
+        ax = plt.gca()
+    else:
+        singlePlot = False
 
-    l = "{} + {}*N + {}*log(N) + {}*Nlog(N)".format(*rcoefs) 
-    legend_elements = [lines.Line2D([0], [0], color='steelblue', label=module),
-                       lines.Line2D([0], [0], color='orange', label=l),
-                       lines.Line2D([0], [0], ls='', label=' R^2='+ str(r2p))]
+    ax.scatter(widths, metric)
+    ax.plot(xp, pred, color='orange')
 
-    _ = plt.plot(widths, ivar, 'o', label=module, markersize=10)
-    _ = plt.plot(xp, pred)
-    _ = plt.legend(handles=legend_elements)
-    _ = plt.xlabel("Width (bits)")
-    _ = plt.ylabel(str.title(var) + units)
-    _ = plt.title("Target frequency " + str(freq) + "MHz")
+    legend_elements = genLegend(fits, coefs, module, r2)
+    ax.legend(handles=legend_elements)
+
+    ax.set_xticks(widths)
+    ax.set_xlabel("Width (bits)")
+    ax.set_ylabel(str.title(var) + units)
+
+    if singlePlot:
+        ax.set_title(module + "  (target  " + str(freq) + "MHz)")
+        plt.show()
+
+def makePlots(mod, freq):
+    fig, axs = plt.subplots(2, 2)
+    plotPPA(mod, freq, 'delay', ax=axs[0,0], fits='cgl')
+    plotPPA(mod, freq, 'area', ax=axs[0,1], fits='clg')
+    plotPPA(mod, freq, 'lpower', ax=axs[1,0], fits='c')
+    plotPPA(mod, freq, 'denergy', ax=axs[1,1], fits='glc')
+    plt.suptitle(mod + "  (target  " + str(freq) + "MHz)")
     plt.show()
 
-def makePlots(mod):
-    plotPPA(mod, 5000, 'delay')
-    plotPPA(mod, 5000, 'area')
-    plotPPA(mod, 10, 'area')
-    plotPPA(mod, 5000, 'lpower')
-    plotPPA(mod, 5000, 'dpower')
+def regress(widths, var, fits='clsgn'):
 
-def regress(widths, var):
+    funcArr = genFuncs(fits)
 
     mat = []
     for w in widths:
-        row = [1, w, np.log(w), w*np.log(w)]
+        row = []
+        for func in funcArr:
+            row += [func(w)]
         mat += [row]
     
     y = np.array(var, dtype=np.float)
     coefsResid = np.linalg.lstsq(mat, y, rcond=None)
     coefs = coefsResid[0]
-    resid = coefsResid[1] 
+    try:
+        resid = coefsResid[1][0]
+    except:
+        resid = 0
     r2 = 1 - resid / (y.size * y.var())
-    return coefs, r2
+    return coefs, r2, funcArr
 
 def makeCoefTable():
     file = open("ppaFitting.csv", "w")
     writer = csv.writer(file)
-    writer.writerow(['Module', 'Metric', 'Freq', '1', 'N', 'log(N)', 'Nlog(N)', 'R^2'])
+    writer.writerow(['Module', 'Metric', 'Freq', '1', 'N', 'N^2', 'log2(N)', 'Nlog2(N)', 'R^2'])
 
     for mod in ['add', 'mult', 'comparator', 'shifter']:
         for comb in [['delay', 5000], ['area', 5000], ['area', 10]]:
             var = comb[0]
             freq = comb[1]
-            widths, ivar, units = getVals(mod, freq, var)
-            coefs, r2 = regress(widths, ivar)
-            row = [mod] + comb + np.ndarray.tolist(coefs) + [r2[0]]
+            widths, metric, units = getVals(mod, freq, var)
+            coefs, r2, funcArr = regress(widths, metric)
+            row = [mod] + comb + np.ndarray.tolist(coefs) + [r2]
             writer.writerow(row)
 
     file.close()
 
+def genFuncs(fits='clsgn'):
+    funcArr = []
+    if 'c' in fits:
+        funcArr += [lambda x: 1]
+    if 'l' in fits:
+        funcArr += [lambda x: x]
+    if 's' in fits:
+        funcArr += [lambda x: x**2]
+    if 'g' in fits:
+        funcArr += [lambda x: np.log2(x)]
+    if 'n' in fits:
+        funcArr += [lambda x: x*np.log2(x)]
+    return funcArr
+
+def noOutliers(freqs, delays, areas):
+    med = statistics.median(freqs)
+    f=[]
+    d=[]
+    a=[]
+    for i in range(len(freqs)):
+        norm = freqs[i]/med
+        if (norm > 0.25) & (norm<1.75):
+            f += [freqs[i]]
+            d += [delays[i]]
+            a += [areas[i]]
+    return f, d, a
+
+def freqPlot(mod, width):
+    freqs = []
+    delays = []
+    areas = []
+    for oneSynth in allSynths:
+        if (mod == oneSynth[0]) & (width == oneSynth[1]):
+            freqs += [oneSynth[2]]
+            delays += [oneSynth[3]]
+            areas += [oneSynth[4]]
+
+    freqs, delays, areas = noOutliers(freqs, delays, areas)
+
+    adprod = np.multiply(areas, delays)
+    adsq = np.multiply(adprod, delays)
+
+    f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, sharex=True)
+    ax1.scatter(freqs, delays)
+    ax2.scatter(freqs, areas)
+    ax3.scatter(freqs, adprod)
+    ax4.scatter(freqs, adsq)
+    ax4.set_xlabel("Freq (MHz)")
+    ax1.set_ylabel('Delay (ns)')
+    ax2.set_ylabel('Area (sq microns)')
+    ax3.set_ylabel('Area * Delay')
+    ax4.set_ylabel('Area * Delay^2')
+    ax1.set_title(mod + '_' + str(width))
+    plt.show()
+
 allSynths = getData()
-
 writeCSV(allSynths)
+# makeCoefTable()
 
-makePlots('shifter')
+freqPlot('comparator', 8)
 
-makeCoefTable()
+# makePlots('shifter', 5000)
 
+# plotPPA('comparator', 5000, 'delay', fits='cls')

synthDC/ppaData.csv

@@ -1,67 +1,197 @@
-Module,Width,Target Freq,Delay,Area,D Power (mW),L Power (nW)
-add,128,10,7.100851,1867.879976,0.00501,465.925
-add,128,5000,0.389771,7007.980119,3.309,2.77
-add,16,10,2.032906,221.479998,0.000575,55.29
-add,16,4000,0.249839,551.74001,0.239,302.479
-add,16,5000,0.228259,924.140017,0.519,641.631
-add,16,6000,0.225754,1120.140018,0.739,1.01
-add,32,10,4.160501,456.679995,0.00118,112.161
-add,32,4000,0.280842,1730.680031,0.735,849.828
-add,32,5000,0.2505,1933.540033,1.049,1.03
-add,32,6000,0.271774,1746.36003,1.138,955.901
-add,64,10,8.474034,927.079988,0.00246,230.083
-add,64,4000,0.323267,3758.300065,1.523,1.75
-add,64,5000,0.334061,3798.480071,1.917,2.18
-add,64,6000,0.328457,3749.480066,2.346,1.77
-add,8,10,0.940062,103.879999,0.000241,24.765
-add,8,5000,0.199689,197.960003,0.113,83.576
-comparator,128,10,0.842074,1997.240039,0.00087,243.506
-comparator,128,5000,0.260142,5215.56005,3.708,6.0
-comparator,16,10,0.576329,252.840005,0.000144,31.402
-comparator,16,4000,0.249312,280.280005,0.0581,55.248
-comparator,16,5000,0.199026,313.600006,0.0859,78.893
-comparator,16,6000,0.166568,422.380007,0.255,301.506
-comparator,32,10,0.765874,495.88001,0.000226,66.41
-comparator,32,4000,0.24995,608.580012,0.168,130.613
-comparator,32,5000,0.205372,919.240014,0.43,840.47
-comparator,32,6000,0.2012,1248.520016,0.928,1.48
-comparator,64,10,0.561562,1008.42002,0.000449,127.626
-comparator,64,4000,0.249905,1437.660027,0.462,558.66
-comparator,64,5000,0.219296,2738.120023,1.989,2.95
-comparator,64,6000,0.221138,2341.220025,1.343,2.59
-comparator,8,10,0.29577,118.580002,6.83e-05,16.053
-comparator,8,5000,0.195502,129.360003,0.0358,21.443
-mult,128,10,9.334627,180734.540854,0.428,1.8
-mult,128,5000,1.78322,314617.244472,997.34,1.63
-mult,16,10,4.730546,3869.040009,0.0107,641.517
-mult,16,4000,0.821111,9132.620147,14.407,8.03
-mult,16,5000,0.820059,9583.420143,20.175,8.5
-mult,16,6000,0.831308,8594.600132,21.106,7.15
-mult,32,10,7.575772,12412.680067,0.0229,1.18
-mult,32,4000,1.091389,31262.980534,65.471,2.49
-mult,32,5000,1.092153,31497.200524,79.554,2.58
-mult,32,6000,1.084816,33519.920555,103.798,2.91
-mult,64,10,4.7933,46798.920227,0.103,5.46
-mult,64,4000,1.411752,93087.261425,227.876,6.05
-mult,64,5000,1.404875,94040.801492,298.667,6.16
-mult,64,6000,1.415466,89931.661403,337.302,5.63
-mult,8,10,2.076433,1009.399998,0.00206,211.637
-mult,8,5000,0.552339,4261.040075,5.543,5.05
-mux2,1,10,0.060639,6.86,5.15e-06,1.19
-mux2,1,10,0.060639,6.86,5.15e-06,1.19
-shifter,128,10,2.758726,9722.580189,0.00789,720.698
-shifter,128,5000,0.401118,19106.080347,6.94,1.23
-shifter,16,10,1.237745,681.100013,0.000441,52.029
-shifter,16,5000,0.209586,2120.720031,1.025,2.15
-shifter,32,10,1.906335,1656.200032,0.00115,118.773
-shifter,32,4000,0.260606,3490.760054,1.282,2.57
-shifter,32,4000,0.260606,3490.760054,1.282,2.57
-shifter,32,4000,0.260606,3490.760054,1.282,2.57
-shifter,32,5000,0.238962,4985.260077,2.489,4.9
-shifter,32,6000,0.241742,4312.000069,2.411,3.71
-shifter,32,6000,0.241742,4312.000069,2.411,3.71
-shifter,32,6000,0.241742,4312.000069,2.411,3.71
-shifter,64,10,2.919486,4346.300085,0.00297,210.734
-shifter,64,5000,0.358993,9471.700156,4.518,6.94
-shifter,8,10,0.622998,244.020005,0.00019,26.943
-shifter,8,5000,0.198885,495.88001,0.285,300.128
+Module,Width,Target Freq,Delay,Area,L Power (nW),D energy (mJ)
+add,128,10,7.100851,1867.879976,465.925,0.0005009999999999999
+add,128,1538,0.633294,4623.64009,632.254,0.00027958387516254874
+add,128,2051,0.486762,4951.940095,885.884,0.0003568990736226231
+add,128,2359,0.423881,5520.340104,1.49,0.00045146248410343363
+add,128,2410,0.414767,5600.700103,1.57,0.00045684647302904563
+add,128,2462,0.406101,5721.240105,1.77,0.0004780666125101544
+add,128,2513,0.397913,6085.800112,2.14,0.0005161161957819339
+add,128,2564,0.436395,6456.240111,2.27,0.0005503120124804992
+add,128,2615,0.390136,6662.040117,2.45,0.0006137667304015296
+add,128,2667,0.394304,7494.060127,3.58,0.00072928383952006
+add,128,2718,0.407908,7287.280117,3.35,0.0006938925680647534
+add,128,2769,0.431383,6941.340124,2.86,0.0006218851570964247
+add,128,3077,0.387515,7712.60013,2.93,0.0007572310692232694
+add,128,3590,0.386891,6860.000114,2.62,0.0006579387186629527
+add,128,5000,0.389771,7007.980119,2.77,0.0006618
+add,16,10,2.032906,221.479998,55.29,5.75e-05
+add,16,2609,0.375085,405.720008,52.28,2.9359908010732082e-05
+add,16,3478,0.287131,443.940009,126.253,4.1978148361127085e-05
+add,16,4000,0.249839,551.74001,302.479,5.9749999999999995e-05
+add,16,4087,0.243761,503.720009,183.936,5.113775385368241e-05
+add,16,4174,0.239287,549.780011,304.811,6.013416387158601e-05
+add,16,4261,0.234402,607.60001,368.742,6.688570758038019e-05
+add,16,4348,0.22992,610.540011,364.173,6.577736890524379e-05
+add,16,4435,0.22545,666.400011,419.709,7.891770011273957e-05
+add,16,4522,0.222724,820.260016,626.379,9.022556390977442e-05
+add,16,4609,0.221986,815.360013,735.998,8.960729008461705e-05
+add,16,4696,0.227412,866.320016,645.684,9.731686541737649e-05
+add,16,5000,0.228259,924.140017,641.631,0.0001038
+add,16,5217,0.22222,824.180016,601.276,8.778991757715163e-05
+add,16,6000,0.225754,1120.140018,1.01,0.00012316666666666666
+add,16,6087,0.226225,857.500013,678.287,0.00010284212255626745
+add,32,10,4.160501,456.679995,112.161,0.00011800000000000001
+add,32,2400,0.41509,958.440019,151.083,6.875e-05
+add,32,3200,0.312424,1121.120021,296.836,0.000105625
+add,32,3680,0.271527,1465.100024,591.825,0.00015000000000000001
+add,32,3760,0.278449,1689.520028,834.387,0.00017898936170212767
+add,32,3840,0.291206,1547.420027,784.112,0.00015859375
+add,32,3920,0.273454,2044.280039,1.33,0.00022066326530612246
+add,32,4000,0.280842,1730.680031,849.828,0.00018375
+add,32,4080,0.256294,1991.360031,1.24,0.00021397058823529412
+add,32,4160,0.253175,2031.540036,1.24,0.00021995192307692308
+add,32,4240,0.268332,1829.660028,1.09,0.00019245283018867924
+add,32,4320,0.254861,1716.960028,866.723,0.0001814814814814815
+add,32,4800,0.258491,1955.100033,1.07,0.00022458333333333334
+add,32,5000,0.2505,1933.540033,1.03,0.00020979999999999998
+add,32,5600,0.254525,1871.800028,877.446,0.0001967857142857143
+add,32,6000,0.271774,1746.36003,955.901,0.00018966666666666665
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+add,64,2909,0.343753,2800.840049,852.781,0.0002536954279821244
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+add,64,3030,0.331556,3202.640054,1.28,0.0003099009900990099
+add,64,3091,0.349251,3284.960053,1.35,0.00031802005823358134
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+add,64,4000,0.323267,3758.300065,1.75,0.00038074999999999996
+add,64,4242,0.328234,3507.420063,1.57,0.00033757661480433756
+add,64,5000,0.334061,3798.480071,2.18,0.0003834
+add,64,6000,0.328457,3749.480066,1.77,0.000391
+add,8,10,0.940062,103.879999,24.765,2.41e-05
+add,8,5000,0.199689,197.960003,83.576,2.26e-05
+comparator,128,10,0.842074,1997.240039,243.506,8.7e-05
+comparator,128,5000,0.260142,5215.56005,6.0,0.0007416
+comparator,16,10000,0.146177,1065.260009,1.61,0.00012470000000000002
+comparator,16,10,0.576329,252.840005,31.402,1.4400000000000001e-05
+comparator,16,5000,0.199026,313.600006,78.893,1.718e-05
+comparator,16,6000,0.166568,422.380007,301.506,4.25e-05
+comparator,32,10000,0.194087,1451.380013,1.85,0.00024430000000000003
+comparator,32,10,0.765874,495.88001,66.41,2.26e-05
+comparator,32,4000,0.24995,608.580012,130.613,4.2000000000000004e-05
+comparator,32,5000,0.205372,919.240014,840.47,8.6e-05
+comparator,32,6000,0.2012,1248.520016,1.48,0.00015466666666666667
+comparator,64,10,0.561562,1008.42002,127.626,4.49e-05
+comparator,64,4000,0.249905,1437.660027,558.66,0.0001155
+comparator,64,5000,0.219296,2738.120023,2.95,0.0003978
+comparator,64,6000,0.221138,2341.220025,2.59,0.00022383333333333332
+comparator,8,10000,0.1136,496.86,810.074,6.46e-05
+comparator,8,10909,0.11361,387.1,565.114,5.885049042075351e-05
+comparator,8,10,0.29577,118.580002,16.053,6.830000000000001e-06
+comparator,8,12727,0.113615,488.039998,768.445,6.364422094759174e-05
+comparator,8,5000,0.195502,129.360003,21.443,7.16e-06
+comparator,8,5455,0.182936,130.340003,22.567,7.259395050412466e-06
+comparator,8,7273,0.13643,147.980003,61.898,1.4711948301938677e-05
+comparator,8,8364,0.119528,210.700003,172.337,2.654232424677188e-05
+comparator,8,8545,0.116724,205.800003,165.947,2.7969572849619658e-05
+comparator,8,8727,0.124671,264.600002,278.768,3.55219433940644e-05
+comparator,8,8909,0.11208,261.660004,251.629,3.5694241777977326e-05
+comparator,8,9091,0.10991,297.920001,343.785,3.882961170388296e-05
+comparator,8,9273,0.107742,309.680003,356.05,4.162622667960746e-05
+comparator,8,9455,0.106411,345.94,438.668,4.569011105235325e-05
+comparator,8,9636,0.111488,397.88,589.556,5.645496056454961e-05
+comparator,8,9818,0.11361,381.219999,573.131,5.265838256264005e-05
+mult,128,10,9.334627,180734.540854,1.8,0.0428
+mult,128,337,2.963253,201889.800086,2.67,0.045112759643916915
+mult,128,449,2.227145,212055.340673,3.27,0.04989086859688196
+mult,128,5000,1.78322,314617.244472,1.63,0.199468
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+mult,128,528,1.893939,255011.682875,6.65,0.10337878787878789
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+mult,128,573,1.745187,296812.604204,1.08,0.14241186736474695
+mult,128,584,1.712328,298800.044147,1.15,0.14923630136986302
+mult,128,596,1.71139,312992.404301,1.44,0.16681040268456376
+mult,128,607,1.707473,305974.624156,1.38,0.1625996705107084
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+mult,128,787,1.735561,317542.544465,1.66,0.19689453621346886
+mult,16,10,4.730546,3869.040009,641.517,0.00107
+mult,16,1122,0.891172,6478.780105,3.54,0.002767379679144385
+mult,16,1146,0.87258,7193.200125,4.57,0.003224258289703316
+mult,16,1171,0.853963,7258.860127,4.57,0.0031195559350982068
+mult,16,1195,0.836814,7685.16012,5.33,0.0032225941422594144
+mult,16,1220,0.81966,8829.800131,6.95,0.0035008196721311477
+mult,16,1244,0.822616,8780.800145,7.15,0.0033842443729903537
+mult,16,1268,0.802449,9789.220166,8.8,0.0038998422712933755
+mult,16,1293,0.813903,9702.000166,8.74,0.0036071152358855374
+mult,16,1317,0.805748,10366.440177,1.01,0.003979498861047836
+mult,16,1463,0.83466,8521.100128,6.71,0.0035974025974025974
+mult,16,1707,0.829615,8563.24013,6.78,0.003674282366725249
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+mult,16,976,1.024406,4960.760064,1.32,0.0011854508196721312
+mult,32,1000,1.099618,29507.800463,2.24,0.015257
+mult,32,10,7.575772,12412.680067,1.18,0.00229
+mult,32,1111,1.092041,31649.100517,2.53,0.01587128712871287
+mult,32,1296,1.097292,30544.640517,2.37,0.015766203703703702
+mult,32,4000,1.091389,31262.980534,2.49,0.01636775
+mult,32,5000,1.092153,31497.200524,2.58,0.0159108
+mult,32,556,1.796075,14371.700056,2.21,0.002714028776978417
+mult,32,6000,1.084816,33519.920555,2.91,0.017299666666666668
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+mult,64,700,1.428547,82949.161302,3.92,0.04733428571428572
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+mult,8,1091,0.915221,1167.180013,211.892,0.00017048579285059578
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+mult,8,1455,0.687251,1615.04003,680.207,0.0004233676975945017
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+mult,8,1709,0.599356,2453.920037,2.01,0.0008338209479227619
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+mult,8,2182,0.550085,4360.02008,5.2,0.0011608615948670944
+mult,8,2545,0.564127,4034.66007,4.58,0.0011772102161100196
+mult,8,5000,0.552339,4261.040075,5.05,0.0011086
+mux2,1,10,0.060639,6.86,1.19,5.149999999999999e-07
+mux2,1,10,0.060639,6.86,1.19,5.149999999999999e-07
+shifter,128,10,2.758726,9722.580189,720.698,0.000789
+shifter,128,5000,0.401118,19106.080347,1.23,0.0013880000000000001
+shifter,16,10,1.237745,681.100013,52.029,4.41e-05
+shifter,16,5000,0.209586,2120.720031,2.15,0.000205
+shifter,32,10,1.906335,1656.200032,118.773,0.000115
+shifter,32,4000,0.260606,3490.760054,2.57,0.0003205
+shifter,32,4000,0.260606,3490.760054,2.57,0.0003205
+shifter,32,4000,0.260606,3490.760054,2.57,0.0003205
+shifter,32,5000,0.238962,4985.260077,4.9,0.0004978
+shifter,32,6000,0.241742,4312.000069,3.71,0.00040183333333333336
+shifter,32,6000,0.241742,4312.000069,3.71,0.00040183333333333336
+shifter,32,6000,0.241742,4312.000069,3.71,0.00040183333333333336
+shifter,64,10,2.919486,4346.300085,210.734,0.000297
+shifter,64,5000,0.358993,9471.700156,6.94,0.0009036
+shifter,8,10,0.622998,244.020005,26.943,1.9e-05
+shifter,8,5000,0.198885,495.88001,300.128,5.6999999999999996e-05

synthDC/ppaFitting.csv

@@ -1,13 +1,13 @@
-Module,Metric,Freq,1,N,log(N),Nlog(N),R^2
-add,delay,5000,0.23935453005464438,0.015973094945355207,-0.058207695467226296,-0.002593789781151714,0.9902532112478974
-add,area,5000,-1032.1274349672115,64.4386855922132,374.6678949053879,-3.2579193244904823,0.9999180068922152
-add,area,10,-13.720004131149423,14.699999256147343,3.6067390521177815e-06,9.312480709428003e-08,1.0
-mult,delay,5000,-0.21755360109289562,-0.00033127390710363004,0.36865114245083547,0.0004100845872014472,0.9999815499619515
-mult,area,5000,-29928.193338752997,-11370.538120558254,39122.3984379376,2592.313970431163,0.9998454828501703
-mult,area,10,-24112.991162714883,-8735.874000034026,30452.017533199683,1892.3032427172166,0.9999575675635335
-comparator,delay,5000,0.18302939890710385,-0.001793523907103751,0.00950014684425352,0.0004195522734073458,0.9999387049502957
-comparator,area,5000,1831.2076391201958,303.59984869227907,-1617.4342555852443,-44.475154143873425,0.9990603962758624
-comparator,area,10,-0.23027509289593326,18.299023530396347,-8.48304611908023,-0.4881808064440773,0.9999674500675539
-shifter,delay,5000,0.4107033934426204,0.03923479405737683,-0.19848886911558317,-0.006549393512462493,0.989283342171845
-shifter,area,5000,-3612.7138133224103,-65.6549821150965,1929.186263038338,35.02443853718661,0.9998392000511572
-shifter,area,10,806.0687632950834,120.52125970491868,-682.1783666753405,-5.1440062238735225,0.9998176364985187
+Module,Metric,Freq,1,N,N^2,log2(N),Nlog2(N),R^2
+add,delay,5000,-0.038978555556527635,-0.08911531250030817,-0.00012953428819478948,0.2083593333340971,0.013950093750045424,1.0
+add,area,5000,-1913.1778463362505,-268.21377075092175,-0.4100347526051751,1046.9667200022955,47.59125331263557,1.0
+add,area,10,-13.720001333167332,14.700000312552621,1.3021426840869221e-09,-1.3062278840780171e-10,-9.375775472819561e-08,1.0
+mult,delay,5000,-0.2915958888891911,-0.02828693750009581,-3.445876736121953e-05,0.32169033333357117,0.0044735312500140964,1.0
+mult,area,5000,27780.605184113756,10418.196477973508,26.857274703166343,-24448.387256089416,-1468.2850310678027,1.0
+mult,area,10,-6472.791005245042,-2075.5787013197305,8.20962684330778,5345.246556351299,313.5693677823146,1.0
+comparator,delay,5000,0.1903951111111219,0.000987500000002994,3.427951388890516e-06,3.333333324460974e-06,-0.00012593750000039925,1.0
+comparator,area,5000,-508.51109056188875,-579.7924890645068,-1.0888888741341944,969.5466443383111,101.5524983752957,1.0
+comparator,area,10,-155.6022268893253,-40.3637507501383,-0.07230902908001494,132.9533363336765,8.452500156270371,1.0
+shifter,delay,5000,0.06953233333235516,-0.08957893750031035,-0.00015877864583368578,0.16727300000076853,0.014763625000045773,1.0
+shifter,area,5000,-237.48663487568587,1208.7075255666841,1.5708073263938906,-1678.7400476770383,-166.69187856311666,1.0
+shifter,area,10,-1079.4155736731122,-591.3687615645423,-0.877491337241916,1211.9333560050677,103.11437703155087,1.0

synthDC/ppaSynth.py

@@ -14,9 +14,13 @@ def deleteRedundant(LoT):
         bashCommand = synthStr.format(*synth)
         outputCPL = subprocess.check_output(['bash','-c', bashCommand])
 
-widths = ['1']
-modules = ['mux2']
-freqs = ['10']
+d = 0.26
+f = 1/d * 1000
+arr = [-40, -20, -8, -6, -4, -2, 0, 2, 4, 6, 8, 20, 40]
+
+widths = ['128']
+modules = ['comparator']
+freqs = [str(round(f+f*x/100)) for x in arr]
 tech = 'sky90'