lrnmap[0, 1] = 0
lrnmap[0, 2] = 1
cfg.core_cfgs[0].lrnmap = lrnmap
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[0, N_INPUTS[0], 2] = 100
W[1, N_INPUTS[0], 1] = 0
print(W)
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, N_INPUTS[0], 2] = True
CW[1, N_INPUTS[0], 1] = True
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Generate spike events (external events)
# rates = np.random.randint(5, 20, (N_INPUTS,), dtype='i')
# rates = np.hstack([np.random.randint(5, 10, (N_INPUTS[0]//2,), 'int'),
# np.random.randint(10, 20, (N_INPUTS[0]//2,), 'int')])
# Build external spikes
freqs = 5
SL = PeriodicPrePostSpikingStimulus(freqs, -5, sim_ticks)
import copy
spk0 = copy.deepcopy(SL)
ext_evts_data = nsat.exportAER(SL)
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
pyST.STCreate.seed(100)
N_CORES = 1 # Number of cores
N_NEURONS = [17] # Number of neurons per core
N_INPUTS = [16] # Number of inputes per core
N_STATES = [8] # Number of states per core
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total number of units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# Transition matrix group 0
cfg.core_cfgs[0].A[0] = [[-3, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[2, -5, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, -7, -5, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, 0, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF]]
# Sign matrix group 0
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1, 1, 1, 1, 1],
[1, -1, 1, 1, 1, 1, 1, 1],
[1, 1, -1, -1, 1, 1, 1, 1],
[1, 1, 1, -1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1,
1], [1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1]]
# Transition matrix group 1
cfg.core_cfgs[0].A[1] = [[0, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[0, -10, OFF, OFF, OFF, OFF, OFF, OFF],
[0, OFF, -8, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF]]
# Sign matrix group 1
cfg.core_cfgs[0].sA[1] = [[-1, 1, 1, 1, 1, 1, 1, 1],
[+1, -1, 1, 1, 1, 1, 1, 1],
[-1, 1, -1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1,
1], [1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1,
1], [1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1]]
# Transition matrix group 2
cfg.core_cfgs[0].A[2] = [[0, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[-3, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, -13, -6, OFF, OFF, OFF, OFF, OFF],
[OFF, -15, OFF, -8, OFF, OFF, OFF, OFF],
[0, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, -7, -8, OFF, OFF],
[OFF, OFF, OFF, OFF, -7, OFF, -3, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, OFF, OFF]]
# Sign matrix group 2
cfg.core_cfgs[0].sA[2] = [[-1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, -1, 1, 1, 1, 1, 1],
[1, -1, 1, -1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, -1, 1, 1],
[1, 1, 1, 1, -1, 1, -1, 1],
[1, 1, 1, 1, 1, 1, 1, 1]]
# Bias
cfg.core_cfgs[0].b[0] = np.array([-12000, 0, -7, 0, 0, 0, 0, 0], 'int')
cfg.core_cfgs[0].b[1] = np.array([-40, 0, 0, 0, 0, 0, 0, 0], dtype='int')
cfg.core_cfgs[0].b[2] = np.array([-40, 0, 0, 0, 0, 0, 0, 0], dtype='int')
# Threshold
cfg.core_cfgs[0].t_ref[0] = 0
# cfg.core_cfgs[0].Xth[0] = 30
# Spike increment value
cfg.core_cfgs[0].XspikeIncrVal[1] = np.array([-1000] + [0] * 7, 'int')
# Additive noise variance
cfg.core_cfgs[0].sigma[0] = np.array([15000] + [0] * 7, 'int')
# refractory period
cfg.core_cfgs[0].t_ref[0] = 0
# Reset value
cfg.core_cfgs[0].Xreset[0] = np.array([0] + [XMAX] * 7, 'int')
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = np.array([True] + [False] * 7, 'bool')
cfg.core_cfgs[0].XresetOn[1] = np.array([False] * 8, 'bool')
cfg.core_cfgs[0].XresetOn[2] = np.array([False] * 8, 'bool')
# Mapping function between neurons and NSAT parameters groups
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
cfg.core_cfgs[0].nmap[-2] = 1
cfg.core_cfgs[0].nmap[-1] = 2
# Synaptic strength
from scipy.linalg import toeplitz
col = np.zeros((N_INPUTS[0] - 1, ))
col[0] = 1
row = np.zeros((N_NEURONS[0] - 2, ))
row[0:3] = np.array([1, 2, 1])
T = toeplitz(col, row)
W = np.zeros((N_UNITS, N_UNITS, N_STATES[0]), 'i')
W[:N_INPUTS[0] - 1, N_INPUTS[0]:-2, 1] = T
W[N_INPUTS[0]:, N_UNITS - 2, 1] = 100
W[N_INPUTS[0]:, N_UNITS - 2, 2] = 100
W[N_INPUTS[0]:, N_UNITS - 1, 2] = 1
W[N_INPUTS[0]:, N_UNITS - 1, 3] = 1
W[N_INPUTS[0] - 1, N_UNITS - 1, 5] = 100
W[N_INPUTS[0] - 1, N_UNITS - 1, 6] = 100
CW = W.astype('bool')
# np.set_printoptions(threshold=np.nan)
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Generate external events firing rates from 5 to 50 inc by 5
freqs = np.random.randint(300, 400, (N_INPUTS[0], ))
SL = RegularSpikingStimulus(freqs, sim_ticks)
# SL.raster_plot()
ext_evts_data = nsat.exportAER(SL)
# Set external events
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_td')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
np.random.seed(30) # Numpy RNG seed
sim_ticks = 5000 # Simulation ticks
N_CORES = 1 # Number of cores
N_NEURONS = [10] # Number of neurons
N_INPUTS = [10] # Number of inputs
N_STATES = [4] # Number of states
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total number of units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# Transition matrix
cfg.core_cfgs[0].A[0] = [[-1, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# Bias
cfg.core_cfgs[0].b[0] = np.array([30, 0, 0, 0], dtype='int')
# Threshold
cfg.core_cfgs[0].Xth[0] = 100
# Reset value
cfg.core_cfgs[0].Xreset[0] = np.array([0, XMAX, XMAX, XMAX], 'int')
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = np.array([True, False, False, False],
'bool')
# Synaptic strengths gain
cfg.core_cfgs[0].Wgain[0] = 1
# Mapping neurons and NSAT parameters groups
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
# Synaptic strengths matrix
W = np.zeros([N_UNITS, N_UNITS, 4], 'int')
W[0, 10, 0] = 50
W[1, 11, 0] = 50
W[2, 12, 0] = 50
W[3, 13, 0] = 50
W[4, 14, 0] = 50
W[5, 15, 0] = 50
W[6, 16, 0] = 50
W[7, 17, 0] = 50
W[8, 18, 0] = 50
W[9, 19, 0] = 50
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, 10, 0] = 1
CW[1, 11, 0] = 1
CW[2, 12, 0] = 1
CW[3, 13, 0] = 1
CW[4, 14, 0] = 1
CW[5, 15, 0] = 1
CW[6, 16, 0] = 1
CW[7, 17, 0] = 1
CW[8, 18, 0] = 1
CW[9, 19, 0] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Generate external events
freqs = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
ext_evts_data = RegularSpikingStimulus(freqs, sim_ticks)
# Set external events
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters files
c_nsat_writer = nsat.C_NSATWriter(cfg,
path='/tmp',
prefix='test_ext_evts_wgain')
c_nsat_writer.write()
# Write Intel FPGA hex parameters files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_ext_evts_wgain')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
sim_ticks = 100 # Simulation time
N_CORES = 2 # Number of cores
N_NEURONS = [1, 1] # Number of neurons per core
N_INPUTS = [5, 5] # Number of inputes per core
N_STATES = [2, 2] # Number of states per core
# Constants
XMAX = nsat.XMAX
OFF = -16
# Class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
for i in range(N_CORES):
# Transition matrix
cfg.core_cfgs[i].A[0] = [[-1, 1], [-1, OFF]]
# Sign matrix
cfg.core_cfgs[i].sA[0] = [[-1, 1], [1, -1]]
# Bias
cfg.core_cfgs[i].b[0] = np.array([0, 0], dtype='int')
# Threshold
cfg.core_cfgs[i].Xth[0] = 100
# Reset value
cfg.core_cfgs[i].Xreset[0] = np.array([0, XMAX], 'int')
# Turn reset on
cfg.core_cfgs[i].XresetOn[0] = np.array([True, False], 'bool')
W = np.zeros((6, 6, 2), dtype='i')
W[0, 5, 0] = 10
W[1, 5, 0] = 10
W[2, 5, 0] = 0
W[3, 5, 0] = 0
W[3, 5, 0] = 0
CW = np.zeros(W.shape, 'i')
CW[0, 5, 0] = 1
CW[1, 5, 0] = 1
CW[2, 5, 0] = 1
CW[3, 5, 0] = 1
CW[3, 5, 0] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[i].wgt_table = wgt_table
cfg.core_cfgs[i].ptr_table = ptr_table
# Mapping function between neurons and NSAT parameters groups
cfg.core_cfgs[i].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
cfg.ext_evts = True
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_davis')
c_nsat_writer.write()
build_davis_file("/tmp/test_davis_davis_events", num_ticks=sim_ticks)
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_NEURONS = [2] # Number of neurons per core
N_INPUTS = [1] # Number of inputs per core
N_STATES = [4] # Number of states per core
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Configuration NSAT class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
monitor_spikes=True,
monitor_weights=True,
plasticity_en=[True],
ben_clock=True)
# Transition matrix group 0
cfg.core_cfgs[0].A[0] = [[-5, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Transition matrix group 1
cfg.core_cfgs[0].A[1] = [[-2, OFF, OFF, OFF], [0, -5, OFF, OFF],
[OFF, OFF, 0, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix group 0
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# Sign matrix group 1
cfg.core_cfgs[0].sA[1] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1],
[1, 1, 1, -1]]
# Threshold
cfg.core_cfgs[0].Xth[0] = 100
cfg.core_cfgs[0].Xth[1] = 175
# Refractory period
cfg.core_cfgs[0].t_ref[1] = 0
cfg.core_cfgs[0].t_ref[0] = 120
# Bias
cfg.core_cfgs[0].b[0] = [5, 0, 0, 0]
cfg.core_cfgs[0].b[1] = [5, 0, 5, 0]
# Initial conditions
cfg.core_cfgs[0].Xinit[0] = np.array([0, 0, 0, 0], 'int')
# Reset value
cfg.core_cfgs[0].Xreset[0] = [0, MAX, MAX, MAX]
cfg.core_cfgs[0].Xreset[1] = [0, MAX, MAX, MAX]
# Turn on reset
cfg.core_cfgs[0].XresetOn[0] = [True, False, False, False]
cfg.core_cfgs[0].XresetOn[1] = [True, False, False, False]
# Enable plasticity per state
cfg.core_cfgs[0].plastic[1] = True
# Enable STDP per state group 0
cfg.core_cfgs[0].stdp_en[0] = True
# Enable STDP per state group 1
cfg.core_cfgs[0].stdp_en[1] = True
# Global modulator state group 1
cfg.core_cfgs[0].modstate[1] = 2
# Set NSAT parameters mapping function
nmap = np.zeros((N_NEURONS[0], ), dtype='int')
nmap[0] = 0
nmap[1] = 1
cfg.core_cfgs[0].nmap = nmap
# Set learning parameters mapping function
lrnmap = np.zeros((nsat.N_GROUPS, N_STATES[0]), dtype='int')
lrnmap[nmap[0], 1] = 0
lrnmap[nmap[1], 1] = 1
cfg.core_cfgs[0].lrnmap = lrnmap
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[0, 1, 0] = 40
W[1, 2, 1] = 50
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, 1, 0] = True
CW[1, 2, 1] = True
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Build external events (spikes)
freqs = [5]
ext_evts_data = RegularSpikingStimulus(freqs, sim_ticks)
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg,
path='/tmp',
prefix='test_two_neurons_stdp')
c_nsat_writer.write()
# Write Intel FPGA parameters hex files
## intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
## prefix='test_two_neurons_stdp')
## intel_fpga_writer.write()
## intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
global SL
print('Begin %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_NEURONS = [1] # Number of neurons per core (list)
N_INPUTS = [1] # Number of inputs per core (list)
N_STATES = [4] # Number of states per core (list)
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total number of units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
monitor_weights=True,
monitor_spikes=True,
plasticity_en=[True],
ben_clock=True)
# Transition matrix A (parameters group 0)
cfg.core_cfgs[0].A[0] = [[-5, OFF, OFF, OFF],
[2, -5, OFF, OFF],
[OFF, OFF, -7, OFF],
[OFF, OFF, OFF, OFF]]
# Transition matrix A (parameters group 1)
cfg.core_cfgs[0].A[1] = [[OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF]]
# Sign matrix A (parameters group 0)
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1],
[1, -1, 1, 1],
[1, 1, -1, 1],
[1, 1, 1, -1]]
# Bias
cfg.core_cfgs[0].b[0] = [0, 0, 0, 0]
# Threshold
cfg.core_cfgs[0].Xth[0] = 25000
# Initial conditions
cfg.core_cfgs[0].Xinit[0] = np.array([[0, 0, 0, 0] for _
in range(N_NEURONS[0])],
'int')
# Reset value
cfg.core_cfgs[0].Xreset[0] = np.array([0, MAX, MAX, MAX], 'int')
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = np.array([True, False, False, False],
'bool')
# STDP kernel height of acausal part
cfg.core_cfgs[0].hiac = [[-1, 4, 0] for _ in range(8)]
cfg.core_cfgs[0].plastic[0] = True # Plastic states group 0
cfg.core_cfgs[0].stdp_en[0] = False # STDP enabled for group 0
cfg.core_cfgs[0].sigma[0] = [0, 0, 5, 0] # Additive noise
cfg.core_cfgs[0].modstate[0] = 2 # Global modulator state
cfg.core_cfgs[0].t_ref[0] = 40 # Refractory period
# Mapping between neurons and parameter groups
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0],), dtype='int')
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[0, 1, 1] = 100
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, 1, 1] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Generate external event spikes (firing rate 50Hz)
stim = [50]
SL = SimSpikingStimulus(stim, t_sim=sim_ticks)
ext_evts_data = nsat.exportAER(SL)
# Set external events
cfg.set_ext_events(ext_evts_data)
# Write the C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_modstate')
c_nsat_writer.write()
# Write Intel FPGA parameters hex files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_modstate')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
global SL
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_test = 2 # Number of tests
Nv = 100 # Visible neurons
Nh = 100 # Hidden neurons
N_NEURONS = [Nh] # Total number of inputes per core
N_INPUTS = [Nv] # Total number of inputs per core
N_STATES = [4] # Number of states per core
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total number of units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# Transition matrix
cfg.core_cfgs[0].A[0] = [[-3, OFF, OFF, OFF], [2, -5, OFF, OFF],
[OFF, OFF, -7, -5], [OFF, OFF, OFF, 0]]
# Sign matrix
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, -1],
[1, 1, 1, -1]]
# Bias
cfg.core_cfgs[0].b[0] = [-12000, 0, -7, 0]
# Refractory period
cfg.core_cfgs[0].t_ref[0] = 40
# Initial conditions
cfg.core_cfgs[0].Xinit[0] = np.zeros((N_STATES[0], ), 'int')
# Reset value
cfg.core_cfgs[0].Xreset[0] = np.array([0, MAX, MAX, MAX], 'int')
# Turn on reset
cfg.core_cfgs[0].XresetOn[0] = np.array([True, False, False, False],
'bool')
# Spike increment value
cfg.core_cfgs[0].XspikeIncrVal[1] = np.array([-1000, 0, 0, 0], 'int')
# Additive noise variance
cfg.core_cfgs[0].sigma[0] = np.array([15000, 0, 0, 0], 'int')
# Set parameters
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
# Set weight matrix
extW = np.zeros([N_INPUTS[0], N_NEURONS[0], N_STATES[0]])
extCW = np.zeros([N_INPUTS[0], N_NEURONS[0], N_STATES[0]])
extW[:Nv, :Nh, 1] = np.eye(N_INPUTS[0]) * 127
extCW[:Nv, :Nh, 1] = np.eye(N_INPUTS[0]).astype('bool')
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[:N_INPUTS[0], N_INPUTS[0]:] = extW
# Set adjacent matrix
CW = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
CW[:N_INPUTS[0], N_INPUTS[0]:] = extCW
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Set external events
stim = np.linspace(1, 1000, N_NEURONS[0])
ext_evts_data = nsat.exportAER(
SimSpikingStimulus(stim, sim_ticks, t_sim=sim_ticks))
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg,
path='/tmp',
prefix='test_sigmoid_ext')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
global SL
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_NEURONS = [1] # Number of neurons per core (list)
N_INPUTS = [1000] # Number of inputs per core (list)
N_STATES = [4] # Number of states per core (list)
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total inputs
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
monitor_spikes=True,
w_check=False,
tstdpmax=[100],
monitor_weights_final=True,
plasticity_en=[True],
ben_clock=True)
# Transition matrix
cfg.core_cfgs[0].A[0] = [[-1, OFF, OFF, OFF], [0, -2, OFF, OFF],
[OFF, OFF, 0, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1],
[1, 1, 1, -1]]
# Refractory period
cfg.core_cfgs[0].t_ref[0] = 20
# Threshold
cfg.core_cfgs[0].Xth[0] = 25
# Bias
cfg.core_cfgs[0].b[0] = [0, 0, 1, 0]
# Initial conditions
cfg.core_cfgs[0].Xinit[0] = np.array([0, 0, 0, 0], 'int')
# Reset value
cfg.core_cfgs[0].Xreset[0] = [0, MAX, MAX, MAX]
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = [True, False, False, False]
# Enable plasticity at states
cfg.core_cfgs[0].plastic[0] = True
# Enable STDP
cfg.core_cfgs[0].stdp_en[0] = True
# Global modulator state
cfg.core_cfgs[0].modstate[0] = 2
# Parameters for the STDP kernel function
# cfg.core_cfgs[0].tstdp = np.array([32 for _ in rNLRN_GROUPS], 'int')
# cfg.core_cfgs[0].tca = np.array([[6, 15] for _ in rNLRN_GROUPS], 'int')
# cfg.core_cfgs[0].hica = np.array([[2, 1, 0] for _ in rNLRN_GROUPS], 'int')
# cfg.core_cfgs[0].sica = np.array([[1, 1, 1] for _ in rNLRN_GROUPS], 'int')
# cfg.core_cfgs[0].tac = np.array([[6, 15] for _ in rNLRN_GROUPS], 'int')
# cfg.core_cfgs[0].hiac = np.array([[-16, -16, 16] for _ in rNLRN_GROUPS],
# 'int')
# cfg.core_cfgs[0].siac = np.array([[-1, -1, -1] for _ in rNLRN_GROUPS],
# 'int')
# Set parameters mapping
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[:N_INPUTS[0], N_INPUTS[0], 1] = 50
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[:N_INPUTS[0], N_INPUTS[0], 1] = True
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Generate spike events (external events)
# rates = np.random.randint(5, 20, (N_INPUTS,), dtype='i')
rates = np.hstack([
np.random.randint(5, 10, (N_INPUTS[0] // 2, ), 'int'),
np.random.randint(10, 20, (N_INPUTS[0] // 2, ), 'int')
])
SL = SimSpikingStimulus(rates, t_sim=sim_ticks)
ext_evts_data = nsat.exportAER(SL)
cfg.set_ext_events(ext_evts_data)
# Write the C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_stdp')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
np.random.seed(30)
sim_ticks = 5000
N_CORES = 2
N_NEURONS = [100, 100]
N_INPUTS = [100, 100]
N_STATES = [4, 4]
N_UNITS = [sum(i) for i in zip(N_INPUTS, N_NEURONS)]
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
for i in range(N_CORES):
cfg.core_cfgs[i].A[0] = [[-1, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
cfg.core_cfgs[i].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# if i == 1:
# cfg.core_cfgs[i].A[0] = [[-2, OFF, OFF, OFF],
# [OFF, OFF, OFF, OFF],
# [OFF, OFF, OFF, OFF],
# [OFF, OFF, OFF, OFF]]
cfg.core_cfgs[i].b[0] = np.array([30, 0, 0, 0], dtype='int')
cfg.core_cfgs[i].Xth[0] = 100
cfg.core_cfgs[i].Xthup[0] = np.array([XMAX, XMAX, XMAX, XMAX], 'int')
cfg.core_cfgs[i].Xthlo[0] = np.ones(4, 'int') * XMIN
cfg.core_cfgs[i].Xreset[0] = np.array([0, XMAX, XMAX, XMAX], 'int')
cfg.core_cfgs[i].XresetOn[0] = np.array([True, False, False, False],
'bool')
for i in range(N_CORES):
# Mapping NSAT parameters (per core)
cfg.core_cfgs[i].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
# Assigning synaptic strength to each core
W = np.zeros([N_UNITS[i], N_UNITS[i], 4], 'int')
tmp = np.zeros((N_INPUTS[i], N_NEURONS[i]))
np.fill_diagonal(tmp, 50)
W[:N_INPUTS[i], N_INPUTS[i]:, 0] = tmp
# Assigning adjacent matrix to each core
CW = np.zeros(W.shape, dtype='bool')
CW[:N_INPUTS[i], N_INPUTS[i]:, 0] = tmp.astype('bool')
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
cfg.core_cfgs[i].wgt_table = wgt_table
cfg.core_cfgs[i].ptr_table = ptr_table
cfg.set_L1_connectivity(({
(0, 102): ((1, 1), (1, 50), (1, 73)),
(0, 103): ((1, 15), (1, 95), (1, 7)),
(0, 105): ((1, 89), (1, 65), (1, 56)),
(0, 143): ((1, 21), (1, 45), (1, 33)),
(0, 113): ((1, 5), (1, 50), (1, 7)),
(0, 133): ((1, 41), (1, 5), (1, 77)),
(0, 123): ((1, 19), (1, 75), (1, 57)),
(0, 104): ((1, 3), )
}))
# Generate external events
# freqs = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
freqs = np.arange(5, 1000, 10)
SL = []
for i in range(1):
SL.append(RegularSpikingStimulus(freqs, sim_ticks))
ext_evts_data = nsat.exportAER(SL)
# Set external events
cfg.set_ext_events(ext_evts_data)
# Generate all the CNSAT parameters files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_routing')
c_nsat_writer.write()
c_nsat_writer.write_L1connectivity()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
sim_ticks = 100 # Simulation time
N_CORES = 1 # Number of cores
N_NEURONS = [2] # Number of neurons per core (list)
N_INPUTS = [0] # Number of input units per core (list)
N_STATES = [4] # Number of states per core (list)
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Instance of main class
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# Define transition matrices
cfg.core_cfgs[0].A[0] = [[-1, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
cfg.core_cfgs[0].A[1] = [[-1, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrices
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
cfg.core_cfgs[0].sA[1] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# Bias
cfg.core_cfgs[0].b[0] = np.array([50, 0, 0, 0], dtype='int')
cfg.core_cfgs[0].b[1] = np.array([0, 0, 0, 0], dtype='int')
# Thresholds
cfg.core_cfgs[0].Xth[0] = 100
cfg.core_cfgs[0].Xth[1] = 100
# Set reset value
cfg.core_cfgs[0].Xreset[0] = np.array([0, XMAX, XMAX, XMAX], 'int')
cfg.core_cfgs[0].Xreset[1] = np.array([0, MAX, MAX, MAX], 'int')
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = np.array([True, False, False, False],
'bool')
cfg.core_cfgs[0].XresetOn[1] = np.array([True, False, False, False],
'bool')
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[0, 1, 0] = 50
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, 1, 0] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Tested value
if len(sys.argv) != 2:
print('Missing argument! Default value is used[prob=0]!')
cfg.core_cfgs[0].prob_syn[1] = np.array([0, 0, 0, 0], dtype='int')
else:
p = int(sys.argv[1])
cfg.core_cfgs[0].prob_syn[1] = np.array([p, p, p, p], 'int')
# NSAT parameters and neurons mapping function
cfg.core_cfgs[0].nmap = np.array([0, 1], dtype='int')
# Write C NSAT parameter files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_prob')
c_nsat_writer.write()
# Write Intel FPGA parameters files
# # intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# # prefix='test_prob')
# # intel_fpga_writer.write()
# # intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
global SL, t_start, t_stop
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_NEURONS = [4] # Number of neurons per core
N_INPUTS = [3] # Number of inputs per core
N_STATES = [4] # Number of states per core
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total number of units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Configuration class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# Transition matrix group 0
cfg.core_cfgs[0].A[0] = [[-7, OFF, OFF, OFF], [0, -5, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Transition matrix group 1
cfg.core_cfgs[0].A[1] = [[-7, OFF, OFF, OFF], [0, -7, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix group 0
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [+1, -1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# Sign matrix group 1
cfg.core_cfgs[0].sA[1] = cfg.core_cfgs[0].sA[0].copy()
# Refractory period group 0
cfg.core_cfgs[0].t_ref[0] = 40
# Refractory period group 1
cfg.core_cfgs[0].t_ref[1] = 30
# Bias group 0
cfg.core_cfgs[0].b[0] = [0, 0, 0, 0]
# Bias group 1
cfg.core_cfgs[0].b[1] = [0, 0, 0, 0]
# Threshold group 0
cfg.core_cfgs[0].Xth[0] = 100
# Threshold group 1
cfg.core_cfgs[0].Xth[1] = 80
# Initial conditions
cfg.core_cfgs[0].Xinit[0] = np.array([0, 0, 0, 0], 'int')
# Reset value group 0
cfg.core_cfgs[0].Xreset[0] = [0, MAX, MAX, MAX]
# Reset value group 1
cfg.core_cfgs[0].Xreset[1] = cfg.core_cfgs[0].Xreset[0].copy()
# Turn reset on group 0
cfg.core_cfgs[0].XresetOn[0] = np.array([True, False, False, False],
'bool')
# Turn reset on group 1
cfg.core_cfgs[0].XresetOn[1] = np.array([True, False, False, False],
'bool')
# Global modulator state group 0
cfg.core_cfgs[0].modstate[0] = 3
# Global modulator state group 0
cfg.core_cfgs[0].modstate[1] = 3
# Parameters groups mapping function
nmap = np.array([0, 0, 0, 1], dtype='int')
cfg.core_cfgs[0].nmap = nmap
# Synaptic weights and adjacent matrix
W, CW = build_synaptic_w(N_INPUTS[0], N_NEURONS[0], N_STATES[0])
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Set external events
rates = [60, 30, 30]
SL = SimSpikingStimulus(rates, t_start, t_stop)
ext_evts_data = nsat.exportAER(SL)
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_wta')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1
N_NEURONS = [100]
N_INPUTS = [100]
N_STATES = [4]
N_UNITS = N_INPUTS[0] + N_NEURONS[0]
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
monitor_spikes=True,
ben_clock=True)
cfg.core_cfgs[0].A[0] = [[-2, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
cfg.core_cfgs[0].b[0] = np.array([-5, 0, 0, 0], dtype='int')
cfg.core_cfgs[0].Xth[0] = 20
cfg.core_cfgs[0].Xthup[0] = np.array([XMAX, XMAX, XMAX, XMAX], 'int')
cfg.core_cfgs[0].Xthlo[0] = np.ones(4, 'int') * XMIN
cfg.core_cfgs[0].Xreset[0] = np.array([0, XMAX, XMAX, XMAX], 'int')
cfg.core_cfgs[0].XresetOn[0] = np.array([False, False, False, False],
'bool')
# Synaptic weights
# Bump
tmp_w = kernel(N_NEURONS[0], amp=(7, 3), sigma=(0.1, 0.35))
# Selection
# tmp_w = kernel(N_NEURONS, amp=(6, 3), sigma=(0.1, 1.0))
# Tracking
# tmp_w = kernel(N_NEURONS, amp=(6, 3), sigma=(0.1, 1.0))
# tmp_w = kernel_(N_NEURONS, (3, 4), (0.01, 0.03))
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
# W[:N_INPUTS, N_INPUTS+40:N_INPUTS+60, 0] = np.ones((20,)) * 30
np.fill_diagonal(W[:N_INPUTS[0], N_INPUTS[0]:, 0], 30)
W[N_INPUTS[0]:, N_INPUTS[0]:, 0] = tmp_w
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
# CW[:N_INPUTS, N_INPUTS+40:N_INPUTS+60, 0] = 1
np.fill_diagonal(CW[:N_INPUTS[0], N_INPUTS[0]:, 0], 1)
CW[N_INPUTS[0]:, N_INPUTS[0]:, 0] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Parameters groups mapping function
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
# Set external events
rates = [20] * N_INPUTS[0]
ext_evts_data, sl = PoissonSpikingStimulus(rates, n_inputs=N_INPUTS[0])
events_i = sl.convert()
mat = np.zeros((sim_ticks, N_INPUTS[0]))
mat[events_i[0].astype('i'), events_i[1].astype('i')] = 1
# ext_evts_data = None
# f = [i for i in np.random.randint(0, 15, (N_INPUTS,))]
# ext_evts_data = RegularSpikingStimulus(f, 300)
cfg.set_ext_events(ext_evts_data)
cfg.core_cfgs[0].latex_print_parameters(1)
# Write C NSAT parameters files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_nf')
c_nsat_writer.write()
# Write FPGA NSAT parameters files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_nf')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
np.random.seed(30) # Numpy random number generator seed
sim_ticks = 5000 # Total simulation time
N_CORES = 2 # Number of cores
N_NEURONS = [10, 10] # Number of neurons per core (list)
N_INPUTS = [10, 10] # Number of inputs per core (list)
N_STATES = [4, 4] # Number of states per core (list)
N_UNITS = [sum(i) for i in zip(N_INPUTS, N_NEURONS)]
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# Loop over the cores and set the parameters
for i in range(N_CORES):
# Transition matrix A
cfg.core_cfgs[i].A[0] = [[-1, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix sA
cfg.core_cfgs[i].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# Bias
cfg.core_cfgs[i].b[0] = np.array([30, 0, 0, 0], dtype='int')
# Threshold
cfg.core_cfgs[i].Xth[0] = 100
# Reset value
cfg.core_cfgs[i].Xreset[0] = np.array([0, XMAX, XMAX, XMAX], 'int')
# Turn reset on per state
cfg.core_cfgs[i].XresetOn[0] = np.array([True, False, False, False],
'bool')
for i in range(N_CORES):
# Mapping NSAT parameters (per core)
cfg.core_cfgs[i].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
if i == 0:
# Assigning synaptic strength to each core
W = np.zeros([N_UNITS[i], N_UNITS[i], 4], 'int')
W[0, 10, 0] = 50
W[1, 11, 0] = 50
W[2, 12, 0] = 50
W[3, 13, 0] = 50
W[4, 14, 0] = 50
W[5, 15, 0] = 50
W[6, 16, 0] = 50
W[7, 17, 0] = 50
W[8, 18, 0] = 50
W[9, 19, 0] = 50
# Assigning adjacent matrix to each core
CW = np.zeros(W.shape, dtype='int')
CW[0, 10, 0] = 1
CW[1, 11, 0] = 1
CW[2, 12, 0] = 1
CW[3, 13, 0] = 1
CW[4, 14, 0] = 1
CW[5, 15, 0] = 1
CW[6, 16, 0] = 1
CW[7, 17, 0] = 1
CW[8, 18, 0] = 1
CW[9, 19, 0] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[i].wgt_table = wgt_table
cfg.core_cfgs[i].ptr_table = ptr_table
if i == 1:
W = np.zeros([N_UNITS[i], N_UNITS[i], 4], 'int')
CW = np.zeros(W.shape, dtype='int')
W[1, 11, 0] = 50
W[5, 15, 0] = 50
CW[1, 11, 0] = 1
CW[5, 15, 0] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[i].wgt_table = wgt_table
cfg.core_cfgs[i].ptr_table = ptr_table
# Connect core 0 neuron 9 with core 1 neurons 1 and 5
cfg.set_L1_connectivity({(0, 9): ((1, 1), (1, 5))})
# Generate external events firing rates from 5 to 50 inc by 5
freqs = [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
SL = []
for i in range(N_CORES - 1):
SL.append(RegularSpikingStimulus(freqs, sim_ticks))
ext_evts_data = nsat.exportAER(SL)
# Set external events
cfg.set_ext_events(ext_evts_data)
# Generate all the CNSAT parameters files
c_nsat_writer = nsat.C_NSATWriter(cfg,
path='/tmp',
prefix='test_external_evts')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_NEURONS = [4] # Number of neurons per core (list)
N_INPUTS = [4] # Number of inputs per core (list)
N_STATES = [4] # Number of states per core (list)
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total inputs
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
tstdpmax=[64],
monitor_states=True,
monitor_spikes=True,
monitor_weights=True,
monitor_weights_final=True,
plasticity_en=[True],
ben_clock=True)
# Transition matrix
cfg.core_cfgs[0].A[0] = [[-1, OFF, OFF, OFF], [0, -1, OFF, OFF],
[OFF, OFF, 0, OFF], [OFF, OFF, OFF, OFF]]
cfg.core_cfgs[0].A[1] = cfg.core_cfgs[0].A[0].copy()
# Sign matrix
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1],
[1, 1, 1, 1]]
cfg.core_cfgs[0].sA[1] = cfg.core_cfgs[0].sA[0].copy()
# Refractory period
cfg.core_cfgs[0].t_ref[0] = 0
cfg.core_cfgs[0].t_ref[1] = 2
# Threshold
cfg.core_cfgs[0].Xth[0] = 100
cfg.core_cfgs[0].Xth[1] = 100
# Bias
cfg.core_cfgs[0].b[0] = [30, 0, 5, 0]
cfg.core_cfgs[0].b[1] = [30, 0, 5, 0]
# Initial conditions
cfg.core_cfgs[0].Xinit[0] = np.array([0, 0, 0, 0], 'int')
cfg.core_cfgs[0].Xinit[1] = np.array([0, 0, 0, 0], 'int')
# Reset value
cfg.core_cfgs[0].Xreset[0] = [0, MAX, MAX, MAX]
cfg.core_cfgs[0].Xreset[1] = [0, MAX, MAX, MAX]
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = [True, False, False, False]
cfg.core_cfgs[0].XresetOn[1] = [True, False, False, False]
# Enable plasticity at states
cfg.core_cfgs[0].plastic[0] = True
cfg.core_cfgs[0].plastic[1] = False
# Enable STDP
cfg.core_cfgs[0].stdp_en[0] = True
cfg.core_cfgs[0].stdp_en[1] = False
# Global modulator state
cfg.core_cfgs[0].modstate[0] = 2
cfg.core_cfgs[0].modstate[1] = 2
# Parameters for the STDP kernel function
rNLRN_GROUPS = list(range(8))
cfg.core_cfgs[0].tstdp = np.array([64 for _ in rNLRN_GROUPS], 'int')
cfg.core_cfgs[0].tca = np.array([[16, 36] for _ in rNLRN_GROUPS], 'int')
cfg.core_cfgs[0].hica = np.array([[1, 0, -1] for _ in rNLRN_GROUPS], 'int')
cfg.core_cfgs[0].sica = np.array([[1, 1, 1] for _ in rNLRN_GROUPS], 'int')
cfg.core_cfgs[0].tac = np.array([[-16, -36] for _ in rNLRN_GROUPS], 'int')
cfg.core_cfgs[0].hiac = np.array([[1, 0, -1] for _ in rNLRN_GROUPS], 'int')
cfg.core_cfgs[0].siac = np.array([[-1, -1, -1] for _ in rNLRN_GROUPS],
'int')
# Set parameters mapping
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
cfg.core_cfgs[0].nmap[0] = 0
cfg.core_cfgs[0].nmap[1] = 1
cfg.core_cfgs[0].nmap[2] = 1
cfg.core_cfgs[0].nmap[3] = 1
# Learning parameters groups mapping
lrnmap = np.zeros((nsat.N_GROUPS, N_STATES[0]), dtype='int')
lrnmap[0, :] = 1
lrnmap[0, 1] = 0
lrnmap[1, 2] = 0
lrnmap[1, 0] = 0
# lrnmap[:] = 0
cfg.core_cfgs[0].lrnmap = lrnmap
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[0, 4, 1] = 50
W[1, 5, 1] = 50
W[2, 6, 1] = 50
W[3, 7, 1] = 50
W[4, 5, 1] = 5
W[4, 6, 1] = 5
W[4, 7, 1] = 5
# Adjacent matrix
CW = W.astype('bool')
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Generate spike events (external events)
# spk_train = np.array([0, 1, 2, 50, 60, 70, 100, 110, 120])
SL = SimSpikingStimulus(t_sim=sim_ticks)
ext_evts_data = nsat.exportAER(SL)
cfg.set_ext_events(ext_evts_data)
# Write the C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_stdp')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
pyST.STCreate.seed(130) # pyNCSre random number generator
np.random.seed(30) # Numpy random number generator
N_CORES = 1 # Number of cores
N_NEURONS = [1] # Number of neurons per core (list)
N_INPUTS = [1] # Number of inputs per core (list)
N_STATES = [8] # Number of states per core (list)
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total number of units
# Number of states (8)
rN_STATES = list(range(N_STATES[0]))
# Number of parameters groups
rN_GROUPS = list(range(nsat.N_GROUPS))
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class isntance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
monitor_spikes=True,
ben_clock=True)
# Transition matrix A
cfg.core_cfgs[0].A[0] = np.array([[-1, OFF, OFF, OFF, OFF, OFF, OFF, OFF],
[-3, -1, OFF, OFF, OFF, OFF, OFF, OFF],
[OFF, -2, -1, OFF, OFF, OFF, OFF, OFF],
[OFF, OFF, -1, -1, OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, -1, -1, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF, -2, -1, OFF, OFF],
[OFF, OFF, OFF, OFF, OFF, -3, -1, OFF],
[OFF, OFF, OFF, OFF, OFF, OFF, 0, -1]],
'int')
# Sign matrix sA
cfg.core_cfgs[0].sA[0] = np.array(
[[-1, 1, 1, 1, 1, 1, 1, 1], [1, -1, 1, 1, 1, 1, 1, 1],
[1, 1, -1, 1, 1, 1, 1, 1], [1, 1, 1, -1, 1, 1, 1, 1],
[1, 1, 1, 1, -1, 1, 1, 1], [1, 1, 1, 1, 1, -1, 1, 1],
[1, 1, 1, 1, 1, 1, -1, 1], [1, 1, 1, 1, 1, 1, 1, -1]], 'int')
# Bias
cfg.core_cfgs[0].b[0] = np.array([0, 0, 0, 0, 0, 0, 0, 0], 'int')
# Initiali conditions
cfg.core_cfgs[0].Xinit[0] = np.array([[0] * 8
for _ in range(N_NEURONS[0])], 'int')
# Reset value
cfg.core_cfgs[0].Xreset[0] = np.array([0, 0, 0, 0, 0, 0, 0, 0], 'int')
# Turn reset on
cfg.core_cfgs[0].XresetOn[0] = np.array(
[True, False, False, False, False, False, False, False], 'bool')
# Synaptic probability
cfg.core_cfgs[0].prob = np.ones(8, dtype='int') * 15
# Threshold
cfg.core_cfgs[0].Xth[0] = 100
# Mapping between neurons and NSAT parameters groups
cfg.core_cfgs[0].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
# Sunaptic weights
W = np.zeros([N_UNITS, N_UNITS, 8], dtype='int')
W[0, 1, 7] = 50
# Adjacent matrix
CW = np.zeros(W.shape, dtype='bool')
CW[0, 1, 7] = True
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Set external events (spikes)
freqs = [15]
ext_evts_data = RegularSpikingStimulus(freqs, ticks=sim_ticks)
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg,
path='/tmp',
prefix='test_eight_states_neuron')
c_nsat_writer.write()
# Write Intel FPGA parameters hex files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_eight_states_neuron')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
global SL
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
np.random.seed(100) # Numpy RNG seed
pyST.STCreate.seed(130) # PyNCS RNG seed
N_CORES = 1 # Number of cores
N_NEURONS = [100] # Number of neurons per core
N_INPUTS = [101] # Number of inputs per core
N_STATES = [4] # Number of states pare core
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
NLRN_GROUPS = 8
N_GROUPS = 8
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
monitor_spikes=True,
monitor_weights=True,
w_check=False,
plasticity_en=np.array([True], 'bool'),
ben_clock=True)
cfg.core_cfgs[core].sigma[0] = [0, 0, 10, 0]
# Transition matrix group 0
cfg.core_cfgs[core].A[0] = [[-5, OFF, OFF, OFF], [3, -5, OFF, OFF],
[OFF, OFF, -6, OFF], [OFF, OFF, OFF, OFF]]
# Transition matrix group 1
cfg.core_cfgs[core].A[1] = [[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix group 0
cfg.core_cfgs[core].sA[0] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1],
[1, 1, 1, -1]]
# Threshold
cfg.core_cfgs[core].Xth[0] = 25000
# Refractory period
cfg.core_cfgs[core].t_ref[0] = 40
# Bias
cfg.core_cfgs[core].b[0] = [0, 0, 0, 0]
# Initial conditions
cfg.core_cfgs[core].Xinit = np.array([[0, 0, 0, 0]
for _ in range(N_NEURONS[0])])
# Reset value
cfg.core_cfgs[core].Xreset[0] = [0, MAX, MAX, MAX]
# Turn reset on
cfg.core_cfgs[core].XresetOn[0] = [True, False, False, False]
# Turn plasticity per state on
cfg.core_cfgs[core].plastic[0] = True
# Turn stdp per state on
cfg.core_cfgs[core].stdp_en[0] = True
cfg.core_cfgs[core].is_stdp_exp_on[0] = True
# Global modulator state
cfg.core_cfgs[core].modstate[0] = 2
# Parameters groups mapping function
cfg.core_cfgs[core].nmap = np.zeros((N_NEURONS[0], ), dtype='int')
lrnmap = 1 + np.zeros((N_GROUPS, N_STATES[0]), dtype='int')
lrnmap[0, 1] = 0
cfg.core_cfgs[core].lrnmap = lrnmap
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[core]], 'int')
W[0:100, N_INPUTS[core]:, 1] = np.eye(N_NEURONS[core]) * 100
W[100, N_INPUTS[core]:, 2] = 100
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0:100, N_INPUTS[core]:, 1] = np.eye(N_NEURONS[core])
CW[100, N_INPUTS[core]:, 2] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[core].wgt_table = wgt_table
cfg.core_cfgs[core].ptr_table = ptr_table
# Learning STDP parameters
cfg.core_cfgs[core].tca = [[24, 48] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].hica = [[-3, -5, -6] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].sica = [[1, 1, 1] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].slca = [[16, 16, 16] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].tac = [[-32, -64] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].hiac = [[-6, -8, -9] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].siac = [[-1, -1, -1] for _ in range(NLRN_GROUPS)]
cfg.core_cfgs[core].slac = [[16, 16, 16] for _ in range(NLRN_GROUPS)]
# Prepare external stimulus (spikes events)
stim = [50] * N_NEURONS[core]
SL = SimSpikingStimulus(stim, t_sim=sim_ticks)
SLr = pyST.SpikeList(id_list=[100])
SLr[100] = pyST.STCreate.inh_poisson_generator(rate=np.array(
[0., 100., 0.]),
t=np.array([0, 400, 600]),
t_stop=sim_ticks)
SL = pyST.merge_spikelists(SL, SLr)
ext_evts_data = nsat.exportAER(SL)
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary file
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_rSTDP')
c_nsat_writer.write()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
N_CORES = 1 # Number of cores
N_NEURONS = [2] # Number of neurons per core
N_INPUTS = [0] # Number of inputs per core
N_STATES = [4] # Number of states per core
N_UNITS = N_INPUTS[0] + N_NEURONS[0] # Total units
# Constants
XMAX = nsat.XMAX
XMIN = nsat.XMIN
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
# Main class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=N_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
w_check=False,
monitor_states=True,
monitor_spikes=True,
monitor_weights=True,
plasticity_en=[True],
ben_clock=True)
# Transition matrix (group 0)
cfg.core_cfgs[0].A[0] = [[-5, OFF, OFF, OFF], [OFF, OFF, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Transition matrix (group 0)
cfg.core_cfgs[0].A[1] = [[-2, OFF, OFF, OFF], [2, -5, OFF, OFF],
[OFF, OFF, OFF, OFF], [OFF, OFF, OFF, OFF]]
# Sign matrix (group 0)
cfg.core_cfgs[0].sA[0] = [[-1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1],
[1, 1, 1, 1]]
# Sign matrix (group 0)
cfg.core_cfgs[0].sA[1] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1],
[1, 1, 1, -1]]
# Refractory period group 0
cfg.core_cfgs[0].t_ref[0] = 0
# Refractory period group 1
cfg.core_cfgs[0].t_ref[1] = 20
# Threshold group 0
cfg.core_cfgs[0].Xth[0] = 100
# Threshold group 1
cfg.core_cfgs[0].Xth[1] = 2500
# Bias group 0
cfg.core_cfgs[0].b[0] = np.array([5, 0, 0, 0], 'int')
# Bias group 1
cfg.core_cfgs[0].b[1] = np.array([5, 0, 5, 0], 'int')
# Initial conditions group 0
cfg.core_cfgs[0].Xinit[0] = np.array([0, 0, 0, 0], 'int')
# Reset value group 0
cfg.core_cfgs[0].Xreset[0] = [0, MAX, MAX, MAX]
# Reset value group 1
cfg.core_cfgs[0].Xreset[1] = [0, MAX, MAX, MAX]
# Turn reset on group 0
cfg.core_cfgs[0].XresetOn[0] = np.array([True, False, False, False],
'bool')
# Turn reset on group 1
cfg.core_cfgs[0].XresetOn[1] = np.array([True, False, False, False],
'bool')
# Enable plastic states for group 1
cfg.core_cfgs[0].plastic[1] = True
# Turn on STDP per state for group 0
cfg.core_cfgs[0].stdp_en[0] = True
# Turn on STDP per state for group 1
cfg.core_cfgs[0].stdp_en[1] = True
# Global modulator state group 0
cfg.core_cfgs[0].modstate[1] = 2
cfg.core_cfgs[0].modstate[0] = 2
# K = 40
# cfg.core_cfgs[0].tstdp[0] = K
# cfg.core_cfgs[0].tca[0] = np.array([K, K])
# cfg.core_cfgs[0].hica[0] = np.array([0, 0, 0])
# cfg.core_cfgs[0].sica[0] = np.array([1, 1, 1])
# cfg.core_cfgs[0].tac[0] = np.array([-K, -K])
# cfg.core_cfgs[0].hiac[0] = np.array([0, 0, 0])
# cfg.core_cfgs[0].siac[0] = np.array([1, 1, 1])
# Parameters groups mapping function
nmap = np.zeros((N_NEURONS[0], ), dtype='int')
nmap[0] = 0
nmap[1] = 1
cfg.core_cfgs[0].nmap = nmap
# Learning parameters groups mapping
lrnmap = np.zeros((nsat.N_GROUPS, N_STATES[0]), dtype='int')
lrnmap[nmap[0], 1] = 0
lrnmap[nmap[1], 1] = 1
lrnmap[:] = 0
cfg.core_cfgs[0].lrnmap = lrnmap
# Synaptic weights
W = np.zeros([N_UNITS, N_UNITS, N_STATES[0]], 'int')
W[0, 1, 1] = 100
# W[1, 0, 1] = 100
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, 1, 1] = 1
# CW[1, 0, 1] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[0].wgt_table = wgt_table
cfg.core_cfgs[0].ptr_table = ptr_table
# Write C NSAT paramters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_iSTDP')
c_nsat_writer.write()
# Write Intel FPGA hex parameters files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_iSTDP')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
return c_nsat_writer.fname
def setup():
print('Begin %s:setup()' %
(os.path.splitext(os.path.basename(__file__))[0]))
OFF = -16
MAX = nsat.MAX
MIN = nsat.XMIN
sim_ticks = 500 # Simulation ticks
NUM_CORES = 1 # Number of cores
N_INPUTS = [0] # Number of inputes per core
N_NEURONS = [1] # Number of neurons per core
N_STATES = [4] # Number of states per core per neuron
# Class instance
cfg = nsat.ConfigurationNSAT(sim_ticks=sim_ticks,
N_CORES=NUM_CORES,
N_INPUTS=N_INPUTS,
N_NEURONS=N_NEURONS,
N_STATES=N_STATES,
monitor_states=True,
ben_clock=True)
# For every core set the parameters
for i in range(NUM_CORES):
# Transition matrix
cfg.core_cfgs[i].A[0] = [[-4, -8, OFF, OFF], [OFF, -7, OFF, OFF],
[0, OFF, -2, OFF], [0, OFF, OFF, -6]]
# Sign matrix
cfg.core_cfgs[i].sA[0] = [[-1, 1, 1, 1], [1, -1, 1, 1], [1, 1, -1, 1],
[1, 1, 1, -1]]
# Adaptive threshold enabled
cfg.core_cfgs[i].flagXth[0] = True
# Threshold
cfg.core_cfgs[i].Xth[0] = MAX
# Bias
cfg.core_cfgs[i].b[0] = np.array([-250, -10, 0, 0], 'int')
# Initial conditions
cfg.core_cfgs[i].Xinit[0] = np.array([-7000, -5000, 100, 10], 'int')
# Reset value
cfg.core_cfgs[i].Xreset[0] = np.array([-7000, -6000, 0, 0], 'int')
# Turn on reset
cfg.core_cfgs[i].XresetOn[0] = np.array([True, False, True, False],
'bool')
# For every core set the synaptic strengths
for i in range(NUM_CORES):
# Synaptic strength
N_UNITS = N_INPUTS[i] + N_NEURONS[i]
W = np.zeros([N_UNITS, N_UNITS, 4], 'int')
W[0, 0, 1] = 5
W[0, 0, 3] = 0
# Adjacent matrix
CW = np.zeros(W.shape, dtype='int')
CW[0, 0, 1] = 1
CW[0, 0, 3] = 1
wgt_table, ptr_table = gen_ptr_wgt_table_from_W_CW(W, CW, [])
np.set_printoptions(threshold=np.nan)
cfg.core_cfgs[i].wgt_table = wgt_table
cfg.core_cfgs[i].ptr_table = ptr_table
# NSAT parameters mapping
cfg.core_cfgs[i].nmap = np.zeros((N_NEURONS[i], ), dtype='int')
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg,
path='/tmp',
prefix='test_mn_neuron')
c_nsat_writer.write()
# Write Intel FPGA parameters hex files
# # intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# # prefix='test_mn_neuron')
# # intel_fpga_writer.write()
# # intel_fpga_writer.write_globals()
cfg.core_cfgs[0].latex_print_parameters(1)
print('End %s:setup()' % (os.path.splitext(os.path.basename(__file__))[0]))
