mirror of
https://github.com/openhwgroup/cvw
synced 2025-01-24 05:24:49 +00:00
Merge branch 'main' of https://github.com/davidharrishmc/riscv-wally into main
This commit is contained in:
David Harris
commit
9080e35e54
@ -525,7 +525,7 @@ module ppa_decoder #(parameter WIDTH = 8) (
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end
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endmodule
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module ppa_mux2_1 #(parameter WIDTH = 1) (
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module ppa_mux2d_1 #(parameter WIDTH = 1) (
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input logic [WIDTH-1:0] d0, d1,
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input logic s,
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output logic [WIDTH-1:0] y);
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@ -533,7 +533,7 @@ module ppa_mux2_1 #(parameter WIDTH = 1) (
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assign y = s ? d1 : d0;
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endmodule
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module ppa_mux4_1 #(parameter WIDTH = 1) (
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module ppa_mux4d_1 #(parameter WIDTH = 1) (
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input logic [WIDTH-1:0] d0, d1, d2, d3,
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input logic [1:0] s,
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output logic [WIDTH-1:0] y);
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@ -541,7 +541,7 @@ module ppa_mux4_1 #(parameter WIDTH = 1) (
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assign y = s[1] ? (s[0] ? d3 : d2) : (s[0] ? d1 : d0);
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endmodule
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module ppa_mux8_1 #(parameter WIDTH = 1) (
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module ppa_mux8d_1 #(parameter WIDTH = 1) (
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input logic [WIDTH-1:0] d0, d1, d2, d3, d4, d5, d6, d7,
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input logic [2:0] s,
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output logic [WIDTH-1:0] y);
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@ -106,7 +106,7 @@ def getVals(tech, module, var, freq=None):
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if (freq != None):
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for oneSynth in allSynths:
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if (oneSynth.freq == freq) & (oneSynth.tech == tech) & (oneSynth.module == module):
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if (oneSynth.freq == freq) & (oneSynth.tech == tech) & (oneSynth.module == module) & (oneSynth.width != 1):
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widthL += [oneSynth.width]
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osdict = oneSynth._asdict()
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metric += [osdict[var]]
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@ -151,33 +151,37 @@ def csvOfBest():
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file.close()
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return bestSynths
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def genLegend(fits, coefs, r2, spec, ale=False):
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''' generates a list of two legend elements
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labels line with fit equation and dots with tech and r squared of the fit
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def genLegend(fits, coefs, r2=None, spec=None, ale=False):
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''' generates a list of two legend elements (or just an equation if no r2 or spec)
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labels line with fit equation and dots with r squared of the fit
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'''
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coefsr = [str(round(c, 3)) for c in coefs]
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eq = ''
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ind = 0
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eqDict = {'c': '', 'l': 'N', 's': '$N^2$', 'g': '$log_2$(N)', 'n': 'N$log_2$(N)'}
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coefsr = [str(sigfig(c, 2)) for c in coefs]
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if ale:
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if (normAddWidth == 32):
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eqDict = {'c': '', 'l': '(N/32)', 's': '$(N/32)^2$', 'g': '$log_2$(N/32)', 'n': '(N/32)$log_2$(N/32)'}
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sub = 'S'
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elif normAddWidth != 1:
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print('Legend equations are wrong')
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print('Equations are wrong, check normAddWidth')
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else:
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sub = 'N'
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eqDict = {'c': '', 'l': sub, 's': '$'+sub+'^2$', 'g': '$log_2$('+sub+')', 'n': ''+sub+'$log_2$('+sub+')'}
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eq = ''
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ind = 0
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for k in eqDict.keys():
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if k in fits:
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if str(coefsr[ind]) != '0.0': eq += " + " + coefsr[ind] + eqDict[k]
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if str(coefsr[ind]) != '0': eq += " + " + coefsr[ind] + eqDict[k]
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ind += 1
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eq = eq[3:] # chop off leading ' + '
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legend_elements = [lines.Line2D([0], [0], color=spec.color, label=eq)]
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legend_elements += [lines.Line2D([0], [0], color=spec.color, ls='', marker=spec.shape, label=spec.tech +' $R^2$='+ str(round(r2, 4)))]
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return legend_elements
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if (r2==None) or (spec==None):
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return eq
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else:
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legend_elements = [lines.Line2D([0], [0], color=spec.color, label=eq)]
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legend_elements += [lines.Line2D([0], [0], color=spec.color, ls='', marker=spec.shape, label='$R^2$='+ str(round(r2, 4)))]
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return legend_elements
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def oneMetricPlot(module, var, freq=None, ax=None, fits='clsgn', norm=True, color=None):
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''' module: string module name
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@ -197,9 +201,14 @@ def oneMetricPlot(module, var, freq=None, ax=None, fits='clsgn', norm=True, colo
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allMetrics = []
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ale = (var != 'delay') # if not delay, must be area, leakage, or energy
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modFit = fitDict[mod]
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modFit = fitDict[module]
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fits = modFit[ale]
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if freq:
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ls = '--'
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else:
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ls = '-'
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for spec in techSpecs:
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metric = getVals(spec.tech, module, var, freq=freq)
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@ -209,26 +218,26 @@ def oneMetricPlot(module, var, freq=None, ax=None, fits='clsgn', norm=True, colo
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metric = [m/norm for m in metric]
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if len(metric) == 5: # don't include the spec if we don't have points for all widths
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xp, pred, coefs, r2 = regress(widths, metric, fits)
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xp, pred, coefs, r2 = regress(widths, metric, fits, ale)
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fullLeg += genLegend(fits, coefs, r2, spec, ale=ale)
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c = color if color else spec.color
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ax.scatter(widths, metric, color=c, marker=spec.shape)
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ax.plot(xp, pred, color=c)
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ax.plot(xp, pred, color=c, linestyle=ls)
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allWidths += widths
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allMetrics += metric
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combined = TechSpec('combined', 'red', '_', 0, 0, 0, 0)
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xp, pred, coefs, r2 = regress(allWidths, allMetrics, fits)
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leg = genLegend(fits, coefs, r2, combined, ale=ale)
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fullLeg += leg
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ax.plot(xp, pred, color='red')
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ax.plot(xp, pred, color='red', linestyle=ls)
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if norm:
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ylabeldic = {"lpower": "Leakage Power (add32)", "denergy": "Energy/Op (add32)", "area": "Area (add32)", "delay": "Delay (FO4)"}
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else:
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ylabeldic = {"lpower": "Leakage Power (nW)", "denergy": "Dynamic Energy (fJ)", "area": "Area (sq microns)", "delay": "Delay (ns)"}
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ax.legend(handles=fullLeg)
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# fullLeg += genLegend(fits, coefs, r2, combined, ale=ale)
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# legLoc = 'upper left' if ale else 'center right'
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# ax.add_artist(ax.legend(handles=fullLeg, loc=legLoc))
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ax.set_xticks(widths)
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ax.set_xlabel("Width (bits)")
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ax.set_ylabel(ylabeldic[var])
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@ -243,15 +252,20 @@ def oneMetricPlot(module, var, freq=None, ax=None, fits='clsgn', norm=True, colo
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ax.set_title(module + titleStr)
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plt.savefig('./plots/PPA/'+ module + '_' + var + '.png')
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# plt.show()
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return fullLeg
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return r2
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def regress(widths, var, fits='clsgn'):
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def regress(widths, var, fits='clsgn', ale=False):
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''' fits a curve to the given points
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returns lists of x and y values to plot that curve and legend elements with the equation
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returns lists of x and y values to plot that curve and coefs for the eq with r2
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'''
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funcArr = genFuncs(fits)
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widths = [w/normAddWidth for w in widths]
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xp = np.linspace(4, 140, 200)
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xpToCalc = xp
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if ale:
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widths = [w/normAddWidth for w in widths]
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xpToCalc = [x/normAddWidth for x in xp]
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mat = []
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for w in widths:
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@ -262,53 +276,91 @@ def regress(widths, var, fits='clsgn'):
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y = np.array(var, dtype=np.float)
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coefs = opt.nnls(mat, y)[0]
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yp = []
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for w in widths:
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n = [func(w) for func in funcArr]
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yp += [sum(np.multiply(coefs, n))]
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r2 = skm.r2_score(y, yp)
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xp = np.linspace(4, 140, 200)
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pred = []
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for x in xp:
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n = [func(x/normAddWidth) for func in funcArr]
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for x in xpToCalc:
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n = [func(x) for func in funcArr]
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pred += [sum(np.multiply(coefs, n))]
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return xp, pred, coefs, r2
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def makeCoefTable():
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'''
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writes CSV with each line containing the coefficients for a regression fit
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''' writes CSV with each line containing the coefficients for a regression fit
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to a particular combination of module, metric (including both techs, normalized)
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'''
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file = open("ppaFitting.csv", "w")
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writer = csv.writer(file)
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writer.writerow(['Module', 'Metric', '1', 'N', 'N^2', 'log2(N)', 'Nlog2(N)', 'R^2'])
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writer.writerow(['Module', 'Metric', 'Target', '1', 'N', 'N^2', 'log2(N)', 'Nlog2(N)', 'R^2'])
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for module in modules:
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for var in ['delay', 'area', 'lpower', 'denergy']:
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ale = (var != 'delay')
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metL = []
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modFit = fitDict[module]
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fits = modFit[ale]
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for freq in [10, None]:
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target = 'easy' if freq else 'hard'
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for var in ['delay', 'area', 'lpower', 'denergy']:
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ale = (var != 'delay')
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metL = []
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modFit = fitDict[module]
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fits = modFit[ale]
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for spec in techSpecs:
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metric = getVals(spec.tech, module, var)
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techdict = spec._asdict()
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norm = techdict[var]
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metL += [m/norm for m in metric]
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for spec in techSpecs:
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metric = getVals(spec.tech, module, var, freq=freq)
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techdict = spec._asdict()
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norm = techdict[var]
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metL += [m/norm for m in metric]
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xp, pred, coefs, r2 = regress(widths*2, metL, fits)
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coefs = np.ndarray.tolist(coefs)
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coefsToWrite = [None]*5
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fitTerms = 'clsgn'
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ind = 0
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for i in range(len(fitTerms)):
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if fitTerms[i] in fits:
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coefsToWrite[i] = coefs[ind]
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ind += 1
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row = [module, var] + coefsToWrite + [r2]
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writer.writerow(row)
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xp, pred, coefs, r2 = regress(widths*2, metL, fits, ale)
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coefs = np.ndarray.tolist(coefs)
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coefsToWrite = [None]*5
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fitTerms = 'clsgn'
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ind = 0
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for i in range(len(fitTerms)):
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if fitTerms[i] in fits:
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coefsToWrite[i] = coefs[ind]
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ind += 1
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row = [module, var, target] + coefsToWrite + [r2]
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writer.writerow(row)
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file.close()
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def sigfig(num, figs):
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return '{:g}'.format(float('{:.{p}g}'.format(num, p=figs)))
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def makeEqTable():
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''' writes CSV with each line containing the equations for fits for each metric
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to a particular module (including both techs, normalized)
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'''
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file = open("ppaEquations.csv", "w")
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writer = csv.writer(file)
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writer.writerow(['Element', 'Best delay', 'Fast area', 'Fast leakage', 'Fast energy', 'Small area', 'Small leakage', 'Small energy'])
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for module in modules:
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eqs = []
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for freq in [None, 10]:
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for var in ['delay', 'area', 'lpower', 'denergy']:
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if (var == 'delay') and (freq == 10):
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pass
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else:
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ale = (var != 'delay')
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metL = []
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modFit = fitDict[module]
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fits = modFit[ale]
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for spec in techSpecs:
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metric = getVals(spec.tech, module, var, freq=freq)
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techdict = spec._asdict()
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norm = techdict[var]
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metL += [m/norm for m in metric]
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xp, pred, coefs, r2 = regress(widths*2, metL, fits, ale)
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coefs = np.ndarray.tolist(coefs)
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eqs += [genLegend(fits, coefs, ale=ale)]
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row = [module] + eqs
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writer.writerow(row)
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file.close()
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@ -369,7 +421,7 @@ def freqPlot(tech, mod, width):
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delays = delaysL[ind]
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freqs = freqsL[ind]
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freqs, delays, areas = noOutliers(median, freqs, delays, areas) # comment out to see all syntheses
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# freqs, delays, areas = noOutliers(median, freqs, delays, areas) # comment out to see all syntheses
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c = 'blue' if ind else 'green'
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# adprod = adprodpow(areas, delays, 1)
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@ -383,14 +435,18 @@ def freqPlot(tech, mod, width):
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lines.Line2D([0], [0], color='blue', ls='', marker='o', label='slack violated')]
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ax1.legend(handles=legend_elements)
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width = str(width)
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ax2.set_xlabel("Target Freq (MHz)")
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ax1.set_ylabel('Delay (ns)')
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ax2.set_ylabel('Area (sq microns)')
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# ax3.set_ylabel('Area * Delay')
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# ax4.set_ylabel('Area * $Delay^2$')
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ax1.set_title(mod + '_' + str(width))
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plt.savefig('./plots/freqBuckshot/' + tech + '/' + mod + '/' + str(width) + '.png')
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ax1.set_title(mod + '_' + width)
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if ('mux' in mod) & ('d' in mod):
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width = mod
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mod = 'muxd'
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plt.savefig('./plots/freqBuckshot/' + tech + '/' + mod + '/' + width + '.png')
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# plt.show()
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def squareAreaDelay(tech, mod, width):
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@ -485,30 +541,35 @@ def plotPPA(mod, freq=None, norm=True, aleOpt=False):
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'''
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plt.rcParams["figure.figsize"] = (10,7)
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fig, axs = plt.subplots(2, 2)
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# fig, axs = plt.subplots(4, 1)
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# oneMetricPlot(mod, 'delay', ax=axs[0], fits=modFit[0], freq=freq, norm=norm)
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# oneMetricPlot(mod, 'area', ax=axs[1], fits=modFit[1], freq=freq, norm=norm)
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# oneMetricPlot(mod, 'lpower', ax=axs[2], fits=modFit[1], freq=freq, norm=norm)
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# oneMetricPlot(mod, 'denergy', ax=axs[3], fits=modFit[1], freq=freq, norm=norm)
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oneMetricPlot(mod, 'delay', ax=axs[0,0], freq=freq, norm=norm)
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oneMetricPlot(mod, 'area', ax=axs[0,1], freq=freq, norm=norm)
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oneMetricPlot(mod, 'lpower', ax=axs[1,0], freq=freq, norm=norm)
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fullLeg = oneMetricPlot(mod, 'denergy', ax=axs[1,1], freq=freq, norm=norm)
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arr = [['delay', 'area'], ['lpower', 'denergy']]
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freqs = [freq]
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if aleOpt: freqs += [10]
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for i in [0, 1]:
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for j in [0, 1]:
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leg = []
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for f in freqs:
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if (arr[i][j]=='delay') and (f==10):
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pass
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else:
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r2 = oneMetricPlot(mod, arr[i][j], ax=axs[i, j], freq=f, norm=norm)
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ls = '--' if f else '-'
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leg += [lines.Line2D([0], [0], color='red', label='$R^2$='+str(round(r2, 4)), linestyle=ls)]
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axs[i, j].legend(handles=leg)
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if aleOpt:
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oneMetricPlot(mod, 'area', ax=axs[0,1], freq=10, norm=norm, color='black')
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oneMetricPlot(mod, 'lpower', ax=axs[1,0], freq=10, norm=norm, color='black')
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oneMetricPlot(mod, 'denergy', ax=axs[1,1], freq=10, norm=norm, color='black')
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titleStr = " (target " + str(freq)+ "MHz)" if freq != None else " (best achievable delay)"
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n = 'normalized' if norm else 'unnormalized'
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saveStr = './plots/PPA/'+ n + '/' + mod + '.png'
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titleStr = " (target " + str(freq)+ "MHz)" if freq != None else ""
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plt.suptitle(mod + titleStr)
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# fig.legend(handles=fullLeg, ncol=3, loc='center', bbox_to_anchor=(0.3, 0.82, 0.4, 0.2))
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fullLeg = [lines.Line2D([0], [0], color='black', label='fastest', linestyle='-')]
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fullLeg += [lines.Line2D([0], [0], color='black', label='smallest', linestyle='--')]
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fig.legend(handles=fullLeg, ncol=3, loc='center', bbox_to_anchor=(0.3, 0.82, 0.4, 0.2))
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if freq != 10: plt.savefig(saveStr)
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if freq != 10:
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n = 'normalized' if norm else 'unnormalized'
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saveStr = './plots/PPA/'+ n + '/' + mod + '.png'
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plt.savefig(saveStr)
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# plt.show()
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def plotBestAreas(mod):
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@ -533,16 +594,17 @@ if __name__ == '__main__':
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##############################
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# set up stuff, global variables
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widths = [8, 16, 32, 64, 128]
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modules = ['priorityencoder', 'add', 'csa', 'shiftleft', 'comparator', 'flop', 'mux2', 'mux4', 'mux8', 'mult']
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modules = ['priorityencoder', 'add', 'csa', 'shiftleft', 'comparator', 'flop', 'mux2', 'mux4', 'mux8', 'mult'] #, 'mux2d', 'mux4d', 'mux8d',]
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normAddWidth = 32 # divisor to use with N since normalizing to add_32
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fitDict = {'add': ['cg', 'l', 'l'], 'mult': ['cg', 's', 'ls'], 'comparator': ['cg', 'l', 'l'], 'csa': ['c', 'l', 'l'], 'shiftleft': ['cg', 'l', 'ln'], 'flop': ['c', 'l', 'l'], 'priorityencoder': ['cg', 'l', 'l']}
|
||||
fitDict = {'add': ['cg', 'l', 'l'], 'mult': ['cg', 'ls', 'ls'], 'comparator': ['cg', 'l', 'l'], 'csa': ['c', 'l', 'l'], 'shiftleft': ['cg', 'l', 'ln'], 'flop': ['c', 'l', 'l'], 'priorityencoder': ['cg', 'l', 'l']}
|
||||
fitDict.update(dict.fromkeys(['mux2', 'mux4', 'mux8'], ['cg', 'l', 'l']))
|
||||
leftblue = [['mux2', 'sky90', 32], ['mux2', 'sky90', 64], ['mux2', 'sky90', 128], ['mux8', 'sky90', 32], ['mux2', 'tsmc28', 8], ['mux2', 'tsmc28', 64]]
|
||||
|
||||
TechSpec = namedtuple("TechSpec", "tech color shape delay area lpower denergy")
|
||||
techSpecs = [['sky90', 'green', 'o', 43.2e-3, 1330.84, 582.81, 520.66], ['tsmc28', 'blue', '^', 12.2e-3, 209.29, 1060, 81.43]]
|
||||
techSpecs = [TechSpec(*t) for t in techSpecs]
|
||||
combined = TechSpec('combined fit', 'red', '_', 0, 0, 0, 0)
|
||||
# invz1arealeakage = [['sky90', 1.96, 1.98], ['gf32', .351, .3116], ['tsmc28', .252, 1.09]] #['gf32', 'purple', 's', 15e-3]
|
||||
##############################
|
||||
|
||||
@ -556,13 +618,14 @@ if __name__ == '__main__':
|
||||
# squareAreaDelay('sky90', 'add', 32)
|
||||
# oneMetricPlot('add', 'delay')
|
||||
# freqPlot('sky90', 'mux4', 16)
|
||||
# plotBestAreas('add')
|
||||
# makeCoefTable()
|
||||
# makeEqTable()
|
||||
|
||||
for mod in ['mux2']: #modules:
|
||||
for mod in modules:
|
||||
plotPPA(mod, norm=False)
|
||||
plotPPA(mod) #, aleOpt=True)
|
||||
# plotBestAreas(mod)
|
||||
# for w in [8, 16, 32, 64, 128]:
|
||||
# freqPlot('sky90', mod, w)
|
||||
# freqPlot('tsmc28', mod, w)
|
||||
plotPPA(mod, aleOpt=True)
|
||||
for w in [8, 16, 32, 64, 128]:
|
||||
freqPlot('sky90', mod, w)
|
||||
freqPlot('tsmc28', mod, w)
|
||||
plt.close('all')
|
||||
11
synthDC/ppaEquations.csv
Normal file
11
synthDC/ppaEquations.csv
Normal file
@ -0,0 +1,11 @@
|
||||
Element,Best delay,Fast area,Fast leakage,Fast energy,Small area,Small leakage,Small energy
|
||||
priorityencoder,0.98$log_2$(N),0.33S,0.25S,0.093S,0.15S,0.046S,0.00046S
|
||||
add,1.8 + 1.4$log_2$(N),1.1S,0.95S,1S,0.34S,0.16S,0.025S
|
||||
csa,3.6,0.93S,1.5S,1.1S,0.34S,0.16S,0.00055S
|
||||
shiftleft,0.48 + 1.6$log_2$(N),1.9S,2.3S,1.5S,0.8S,0.29S,0.0059S
|
||||
comparator,2 + 0.94$log_2$(N),0.6S,0.47S,0.31S,0.34S,0.16S,0.00089S
|
||||
flop,3.3,0.34S,0.37S,0.0012S,0.34S,0.37S,0.0012S
|
||||
mux2,2.8 + 0.38$log_2$(N),0.2S,0.18S,0.16S,0.15S,0.12S,0.0011S
|
||||
mux4,3.1 + 0.51$log_2$(N),0.36S,0.32S,0.28S,0.28S,0.11S,0.0021S
|
||||
mux8,5 + 0.45$log_2$(N),0.76S,0.66S,0.45S,0.55S,0.24S,0.0029S
|
||||
mult,6$log_2$(N),13S + 10$S^2$,26S + 7.3$S^2$,42S + 25$S^2$,1.1S + 7.9$S^2$,1S + 3.4$S^2$,2.1$S^2$
|
||||
|
@ -74,7 +74,7 @@ if { $saifpower == 1 } {
|
||||
if {$drive != "INV"} {
|
||||
set_false_path -from [get_ports reset]
|
||||
}
|
||||
if {(($::env(DESIGN) == "ppa_mux2_1") || ($::env(DESIGN) == "ppa_mux4_1") || ($::env(DESIGN) == "ppa_mux8_1"))} {
|
||||
if {(($::env(DESIGN) == "ppa_mux2d_1") || ($::env(DESIGN) == "ppa_mux4d_1") || ($::env(DESIGN) == "ppa_mux8d_1"))} {
|
||||
set_false_path -from {s}
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user