def objective(val, Pyx=Pyx, x=x, y=y, nx=nx, ny=ny):
global C_equal, C_equal_bounds
xmin, xmax, ymin, ymax = val
xinputs = linspace(xmin, xmax, nx)
ydividers = linspace(ymin, ymax, ny+1)
ydividers = ydividers[1:-1]
Pyx_sub, x_sub, y_sub = blahut.quantize(Pyx, x, y, xinputs, ydividers)
C, Px = blahut.blahut_arimoto(Pyx_sub,
tolerance=1e-7,
iterations = 100)
if C > C_equal[ix, iy]:
C_equal[ix, iy] = C
C_equal_bounds[ix, iy, :] = (xmin, xmax, ymin, ymax)
data = dict(C_equal=C_equal,
C_equal_bounds=C_equal_bounds)
savez('./../npz/C_equal_optimized.npz', data=data)
print '\nC:', C
print 'nx:', nx, xmin, xmax
print 'ny:', ny, ymin, ymax
return -C
def objective(val, Pyx=Pyx, x=x, y=y, nx=nx, ny=ny):
global C_nonequal, x_in, y_out, Px_in
xinputs = val[0:nx]
ydividers = val[nx:]
xinputs.sort()
ydividers.sort()
Pyx_sub, x_sub, y_sub = blahut.quantize(Pyx, x, y, xinputs, ydividers)
C, Px = blahut.blahut_arimoto(Pyx_sub,
tolerance=1e-7,
iterations = 1000)
if C > C_nonequal[ix, iy]:
C_nonequal[ix, iy] = C
x_in[ix][iy] = xinputs
y_out[ix][iy] = ydividers
Px_in[ix][iy] = Px
data = dict(C_nonequal=C_nonequal,
x_in = x_in,
y_out = y_out,
Px_in = Px_in)
savez('./../npz/C_nonequal_optimized.npz', data=data)
print '\nC:', C
print 'x:', nx, xinputs
print 'y:', ny, ydividers
return -C
def objective(val, Pyx=Pyx, x=x, y=y, idx=idx):
xinputs = val
xinputs.sort()
Pyx_sub, x_sub, y_sub = blahut.quantize(Pyx, x, y, xinputs, ydividers=None)
C, Px = blahut.blahut_arimoto(Pyx_sub,
tolerance=1e-4,
iterations = 100)
result = {'C_in':C, 'Px_in':Px, 'x_in':xinputs}
helpers.add_to_database( result=result, index=idx,
goal=('C_in', 'max'), filename=filename )
print '\nC:', C
print 'x_in:', xinputs
print 'idx:', idx
return -C
Pyx, x, y = blahut.Q(V, R, nx=2000, ny=2000)
#==============================================================================
# %% Calculate Constrained Capacity
#==============================================================================
f = load('./../data/transistorFD12.npz')
Vg = f['Vg']
I = f['Isd'].T[-1]
I_spline = interpolate.UnivariateSpline(Vg, I)
Ix = I_spline(x)
Vsd = 4
t_reset = 300e-9 #seconds
energy = (Ix * Vsd * t_reset * 1e12) / 2 #pJ
#s go from 1e-2 to 1
n = 10
s_list = logspace(-4, -1, n)
func = lambda s: blahut.blahut_arimoto(Pyx=Pyx,
tolerance=1e-3,
iterations=10,
e=energy,
s=s,
debug=True)
iters = [('s', s_list)]
outs = ['C','Px', 'E']
res = helpers.loop(func, iters, outs, savefile='./../npz/Cenergy_loop.npz')
#==============================================================================
#%% Load and preformat the data
#Derive 2-D Density with Linear Interpolation of Gaussian KDE
#==============================================================================
data = load('./../data/IEDM2014_PCM_Partial_Reset.npz')
helpers.dict2global(data)
Pyx, x, y = blahut.Q(V, R, nx=2000, ny=2000)
#==============================================================================
# %% Calculate Constrained Capacity
#==============================================================================
f = load('./../data/transistorFD12.npz')
Vg = f['Vg']
I = f['Isd'].T[-1]
I_spline = interpolate.UnivariateSpline(Vg, I)
Ix = I_spline(x)
Vsd = 4
t_reset = 300e-9 #seconds
energy = (Ix * Vsd * t_reset * 1e12) / 2 #pJ
#s go from 1e-2 to 1
n = 10
s_list = logspace(-4, -1, n)
func = lambda s: blahut.blahut_arimoto(
Pyx=Pyx, tolerance=1e-3, iterations=10, e=energy, s=s, debug=True)
iters = [('s', s_list)]
outs = ['C', 'Px', 'E']
res = helpers.loop(func, iters, outs, savefile='./../npz/Cenergy_loop.npz')
