ReferenceUnitCell)
from aihwkit.simulator.rpu_base import cuda
# Prepare the datasets (input and expected output).
x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
y = Tensor([[1.0, 0.5], [0.7, 0.3]])
# Define a single-layer network, using a vector device having multiple
# devices per crosspoint. Each device can be arbitrarily defined
rpu_config = UnitCellRPUConfig()
# 3 arbitrary devices per cross-point.
rpu_config.device = VectorUnitCell(
unit_cell_devices=[
ReferenceUnitCell(unit_cell_devices=[SoftBoundsDevice(w_max=1.0)]),
ConstantStepDevice(),
LinearStepDevice(w_max_dtod=0.4),
SoftBoundsDevice()
])
# Only one of the devices should receive a single update.
# That is selected randomly, the effective weights is the sum of all
# weights.
rpu_config.device.update_policy = VectorUnitCellUpdatePolicy.SINGLE_RANDOM
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
print(rpu_config)
# Move the model and tensors to cuda if it is available.
VectorUnitCell
)
from aihwkit.simulator.rpu_base import cuda
# Prepare the datasets (input and expected output).
x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
y = Tensor([[1.0, 0.5], [0.7, 0.3]])
# Define a single-layer network, using a vector device having multiple
# devices per crosspoint. Each device can be arbitrarily defined
rpu_config = UnitCellRPUConfig()
# 3 arbitrary single unit cell devices (of the same type) per cross-point.
rpu_config.device = VectorUnitCell(
unit_cell_devices=[
ConstantStepDevice(w_max=0.3),
ConstantStepDevice(w_max_dtod=0.4),
ConstantStepDevice(up_down_dtod=0.1),
])
# Only one of the devices should receive a single update.
# That is selected randomly, the effective weights is the sum of all
# weights.
rpu_config.device.update_policy = VectorUnitCellUpdatePolicy.SINGLE_RANDOM
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
print(rpu_config)
# Move the model and tensors to cuda if it is available.
if cuda.is_compiled():
LinearStepDevice,
SoftBoundsDevice)
from aihwkit.simulator.rpu_base import cuda
# Prepare the datasets (input and expected output).
x = Tensor([[0.1, 0.2, 0.4, 0.3], [0.2, 0.1, 0.1, 0.3]])
y = Tensor([[1.0, 0.5], [0.7, 0.3]])
# Define a single-layer network, using a vector device having multiple
# devices per crosspoint. Each device can be arbitrarily defined
rpu_config = UnitCellRPUConfig()
# 3 arbitrary devices per cross-point.
rpu_config.device = VectorUnitCellDevice(unit_cell_devices=[
ConstantStepDevice(),
LinearStepDevice(w_max_dtod=0.4),
SoftBoundsDevice()
])
# Only one of the devices should receive a single update.
rpu_config.device.single_device_update = True
# That is selected randomly, the effective weights is the sum of all
# weights.
rpu_config.device.single_device_update_random = True
model = AnalogLinear(4, 2, bias=True, rpu_config=rpu_config)
print(model.analog_tile.tile)
# Move the model and tensors to cuda if it is available.
if cuda.is_compiled():