aihwkit.simulator.configs.devices module

Configuration for NVM devices for Analog (Resistive Device) tiles.

class aihwkit.simulator.configs.devices.ConstantStepDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False)[source]

Bases: PulsedDevice

Pulsed update behavioral model: constant step.

Pulsed update behavioral model, where the update step of material is constant throughout the resistive range (up to hard bounds).

In more detail, the update behavior implemented for ConstantStep is:

\[ \begin{align}\begin{aligned}w_{ij} &\leftarrow& w_{ij} - \Delta w_{ij}^d(1 + \sigma_\text{c-to-c}\,\xi)\\w_{ij} &\leftarrow& \text{clip}(w_{ij},b^\text{min}_{ij},b^\text{max}_{ij})\end{aligned}\end{align} \]

where \(d\) is the direction of the update (product of signs of input and error). \(\Delta w_{ij}^d\) is the update step size of the cross-point ij in direction \(d\) (up or down). Note that each cross-point has separate update sizes so that device-to-device fluctuations and biases in the directions can be given.

Moreover, the clipping bounds of each cross-point ij (i.e. \(b_{ij}^\text{max/min}\)) are also different in general. The mean and the amount of systematic spread from device-to-device can be given as parameters, see below.

For parameters regarding the devices settings, see e.g. PulsedDevice.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

bindings_class: ClassVar[Type | str | None] = 'ConstantStepResistiveDeviceParameter'
class aihwkit.simulator.configs.devices.ExpStepDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, A_up=0.00081, A_down=0.36833, gamma_up=12.44625, gamma_down=12.78785, a=0.244, b=0.2425, dw_min_std_add=0.0, dw_min_std_slope=0.0, write_noise_std=0.0, apply_write_noise_on_set=True)[source]

Bases: PulsedDevice

Exponential update step or CMOS-like update behavior.

This model is derived from PulsedDevice and uses all its parameters. ExpStepDevice only implements a new ‘update once’ functionality, where the minimal weight step change with weight is fitted by an exponential function as detailed below.

\[w_{ij} \leftarrow w_{ij} - \max(y_{ij},0) \Delta w_{ij}^d (1 + \sigma_\text{c-to-c}\,\xi)\]

and \(y_{ij}\) is given as

\[ \begin{align}\begin{aligned}z_{ij} = 2 a_\text{es} \frac{w_{ij}}{b^\text{max}_{ij} - b^\text{min}_{ij}} + b_\text{es}\\y_{ij} = 1 - A^{(d)} e^{d \gamma^{(d)} z_{ij}}\end{aligned}\end{align} \]

where \(d\) is the direction of the update (+ or -), see also PulsedDevice for details.

All additional parameter (\(a_\text{es}\), \(b_\text{es}\), \(\gamma^{(d)}\), \(A^{(d)}\) ) are tile-wise fitting parameters (ie. no device-to-device variation in these parameters). Note that the other parameter involved can be still defined with device-to-device variation and (additional) up-down bias (see PulsedDevice).

Note

This device also features a more complex cycle-to-cycle noise model of the update step, when specifying dw_min_std_add and dw_min_std_slope. By default, The Gaussian noise added to the calculated update step size \(\Delta q_\text{act}\) is proportional to

\[\sigma_{\text{final}} = \sigma \left( \sigma_\text{add} + |\Delta w_\text{actual}| + \sigma_\text{slope} |w_\text{current}|\right)\]

where the \(\sigma\) is given by dw_min_std, \(\sigma_\text{add}\) is given by dw_min_std_add, and \(\sigma_\text{slope}\) is given by dw_min_std_slope.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • A_up (float) –

  • A_down (float) –

  • gamma_up (float) –

  • gamma_down (float) –

  • a (float) –

  • b (float) –

  • dw_min_std_add (float) –

  • dw_min_std_slope (float) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

A_down: float = 0.36833

Factor A for the down direction.

A_up: float = 0.00081

Factor A for the up direction.

a: float = 0.244

Global slope parameter.

apply_write_noise_on_set: bool = True

Whether setting the weights with set_weights will add write noise to the apparent weight state or not.

If False the persistent weight state will be equal to the apparent state initially.

b: float = 0.2425

Global offset parameter.

bindings_class: ClassVar[Type | str | None] = 'ExpStepResistiveDeviceParameter'
dw_min_std_add: float = 0.0

additive cycle-to-cycle noise of the update size (in units of dw_min_std, see above).

dw_min_std_slope: float = 0.0

cycle-to-cycle noise of the update size (in units of dw_min_std, see above).

gamma_down: float = 12.78785

Exponent for the down direction.

gamma_up: float = 12.44625

Exponent for the up direction.

write_noise_std: float = 0.0

Whether to use update write noise.

Whether to use update write noise that is added to the updated devices weight, while the update is done on a hidden persistent weight. The update write noise is then sampled a new when the device is touched again.

Thus it is:

\[w_\text{apparent}{ij} = w_{ij} + \sigma_\text{write_noise}\xi\]

and the update is done on \(w_{ij}\) but the forward sees the \(w_\text{apparent}\).

class aihwkit.simulator.configs.devices.FloatingPointDevice(diffusion=0.0, lifetime=0.0, drift=<factory>)[source]

Bases: _PrintableMixin

Floating point reference.

Implements ideal devices forward/backward/update behavior.

Parameters:
as_bindings(data_type)[source]

Return a representation of this instance as a simulator bindings object.

Parameters:

data_type (RPUDataType) –

Return type:

Any

bindings_class: ClassVar[Type | str | None] = 'FloatingPointTileParameter'
bindings_module: ClassVar[str] = 'devices'
diffusion: float = 0.0

Standard deviation of diffusion process.

drift: SimpleDriftParameter

Parameter governing a power-law drift.

lifetime: float = 0.0

One over decay_rate, ie \(1/r_\text{decay}\).

requires_decay()[source]

Return whether device has decay enabled.

Return type:

bool

requires_diffusion()[source]

Return whether device has diffusion enabled.

Return type:

bool

class aihwkit.simulator.configs.devices.IdealDevice(construction_seed=0, diffusion=0.0, lifetime=0.0, reset_std=0.01)[source]

Bases: _PrintableMixin

Ideal update behavior (using floating point), but forward/backward might be non-ideal.

Ideal update behavior (using floating point), however, forward/backward might still have a non-ideal ADC or noise added.

Parameters:

  • construction_seed (int) –

  • diffusion (float) –

  • lifetime (float) –

  • reset_std (float) –

as_bindings(data_type)[source]

Return a representation of this instance as a simulator bindings object.

Parameters:

data_type (RPUDataType) –

Return type:

Any

bindings_class: ClassVar[Type | str | None] = 'IdealResistiveDeviceParameter'
bindings_module: ClassVar[str] = 'devices'
construction_seed: int = 0

If not 0, set a unique seed for hidden parameters during construction.

diffusion: float = 0.0

Standard deviation of diffusion process.

lifetime: float = 0.0

One over decay_rate, ie \(1/r_\text{decay}\).

requires_decay()[source]

Return whether device has decay enabled.

Return type:

bool

requires_diffusion()[source]

Return whether device has diffusion enabled.

Return type:

bool

reset_std: float = 0.01

Standard deviation around zero mean in case reset is called.

class aihwkit.simulator.configs.devices.LinearStepDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, gamma_up=0.0, gamma_down=0.0, gamma_up_dtod=0.05, gamma_down_dtod=0.05, allow_increasing=False, mean_bound_reference=True, mult_noise=True, write_noise_std=0.0, apply_write_noise_on_set=True, reverse_up=False, reverse_down=False, reverse_offset=0.01)[source]

Bases: PulsedDevice

Pulsed update behavioral model: linear step.

Pulsed update behavioral model, where the update step response size of the material is linearly dependent with resistance (up to hard bounds).

This model is based on PulsedDevice and thus shares all parameters and functionality. In addition, it only implements a more general update once function, where the update step size can depend linearly on the weight itself.

For each coincidence the weights is updated once. Here, the positive (negative) update step size decreases linearly in the following manner (compare to the update once for ConstantStepDevice):

\begin{eqnarray*} w_{ij} &\leftarrow& w_{ij} - \Delta w_{ij}^d(\gamma_{ij}^d\;w_{ij} + 1 + \sigma_\text{c-to-c}\,\xi)\\ w_{ij} &\leftarrow& \text{clip}(w_{ij},b^\text{min}_{ij},b^\text{max}_{ij}) \end{eqnarray*}

in case of additive noise. Optionally, multiplicative noise can be chosen in which case the first equation becomes:

\[w_{ij} \leftarrow w_{ij} - \Delta w_{ij}^d (\gamma_{ij}^d \;w_{ij} + 1) (1 + \sigma_\text{c-to-c}\,\xi)\]

The cross-point ij dependent slope parameter \(\gamma_{ij}^d\) are given during initialization by

\begin{eqnarray*} \gamma_{ij}^+ &\=& - |\gamma^+ + \gamma_\text{d-to-d}^+ \xi|/b^\text{max}_{ij}\\ \gamma_{ij}^- &\=& - |\gamma^- + \gamma_\text{d-to-d}^- \xi|/b^\text{min}_{ij} \end{eqnarray*}

where the \(\xi\) are standard Gaussian random variables and \(b^\text{min}_{ij}\) and \(b^\text{max}_{ij}\) the cross-point ij specific minimal and maximal weight bounds, respectively (see description for PulsedDevice).

Note

If \(\gamma=1\) and \(\gamma_\text{d-to-d}=0\) this update implements soft bounds, since the updates step becomes equal to \(1/b\).

Note

If \(\gamma=0\) and \(\gamma_\text{d-to-d}=0\) and additive noise, this update is identical to those described in PulsedDevice.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • gamma_up (float) –

  • gamma_down (float) –

  • gamma_up_dtod (float) –

  • gamma_down_dtod (float) –

  • allow_increasing (bool) –

  • mean_bound_reference (bool) –

  • mult_noise (bool) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

  • reverse_up (bool) –

  • reverse_down (bool) –

  • reverse_offset (float) –

allow_increasing: bool = False

Whether to allow increasing of update sizes.

Whether to allow the situation where update sizes increase towards the bound instead of saturating (and thus becoming smaller).

apply_write_noise_on_set: bool = True

Whether setting the weights with set_weights will add write noise to the apparent weight state or not.

If False the persistent weight state will be equal to the apparent state initially.

bindings_class: ClassVar[Type | str | None] = 'LinearStepResistiveDeviceParameter'
gamma_down: float = 0.0

The value of \(\gamma^-\).

gamma_down_dtod: float = 0.05

Device-to-device variation for \(\gamma^-\), i.e. the value of \(\gamma_\text{d-to-d}^-\).

gamma_up: float = 0.0

The value of \(\gamma^+\).

Intuitively, a value of 0.1 means that the update step size in up direction at the weight bounds is 10% decreased relative to that origin \(w=0\).

Note

In principle one could fix \(\gamma=\gamma^-=\gamma^+\) since up/down variation can be given by up_down_dtod, see PulsedDevice.

Note

The hard-bounds are still observed, so that the weight cannot grow beyond its bounds.

gamma_up_dtod: float = 0.05

Device-to-device variation for \(\gamma^+\), i.e. the value of \(\gamma_\text{d-to-d}^+\).

mean_bound_reference: bool = True

Whether to use instead of the above:

\[ \begin{align}\begin{aligned}\gamma_{ij}^+ &\=& - |\gamma^+ + \gamma_\text{d-to-d}^+ \xi|/b^\text{max}\\\gamma_{ij}^- &\=& - |\gamma^- + \gamma_\text{d-to-d}^- \xi|/b^\text{min}\end{aligned}\end{align} \]

where \(b^\text{max}\) and \(b^\text{max}\) are the values given by w_max and w_min, see PulsedDevice.

mult_noise: bool = True

Whether to use multiplicative noise instead of additive cycle-to-cycle noise.

reverse_down: bool = False

Whether to increase the step size in down direction with decreasing weights (default decreases).

Note

If set, mult_noise needs to be also set.

reverse_offset: float = 0.01

Offset to add to the step size for reverse up or down to avoid zero step size at weight min or max.

reverse_up: bool = False

Whether to increase the step size in up direction with increasing weights (default decreases).

Note

If set, mult_noise needs to be also set.

write_noise_std: float = 0.0

Whether to use update write noise.

Whether to use update write noise that is added to the updated devices weight, while the update is done on a hidden persistent weight. The update write noise is then sampled anew when the device is touched again.

Thus it is:

\[w_\text{apparent}{ij} = w_{ij} + \sigma_\text{write_noise} \Delta w_\text{min}\xi\]

and the update is done on \(w_{ij}\) but the forward sees the \(w_\text{apparent}\).

class aihwkit.simulator.configs.devices.PiecewiseStepDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, piecewise_up=<factory>, piecewise_down=<factory>, write_noise_std=0.0, apply_write_noise_on_set=True)[source]

Bases: PulsedDevice

Piece-wise interpolated device update characteristics.

This model is derived from PulsedDevice and uses all its parameters. PiecewiseStepDevice implements a new functionality where the device’s update response curve is given explicitly on nodes over the weight range. The device will automatically interpolate the update step size using the given node values.

In detail, the update in down direction of the device is given as:

\[w_{ij} \leftarrow w_{ij} + \Delta w_{ij}^d \left((1 - q) v^d(i_w) + q \, v^d(i_w + 1)\right) (1 + \sigma_\text{c-to-c}\,\xi)\]

where \(i_w\) is the index of the given vector \(v^d\) (piecewise_down) where the current weight value would fall into if scaled to the current min and max values of that device (first and last value are set at weight min and max, respectively, the other values are equally distributed in the range).

The scalar \(q\) is the relative position of the weight in the current segment between the two selected nodes \(v^d(i_w)\) and \(v^d(i_w + 1)\).

The update in up direction is computed analogously using the piecewise_up vector.

Note

The piecewise up and down vectors need to have the same number of elements.

Note

In case of GPUs the maximal number of nodes in the vectors is limited to below 64 due to performance reasons.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • piecewise_up (List[float]) –

  • piecewise_down (List[float]) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

apply_write_noise_on_set: bool = True

Whether setting the weights with set_weights will add write noise to the apparent weight state or not.

If False the persistent weight state will be equal to the apparent state initially.

bindings_class: ClassVar[Type | str | None] = 'PiecewiseStepResistiveDeviceParameter'
piecewise_down: List[float]

Array of values that characterize the update steps in downwards direction.

Analogous to piecewise_up but for the downwards direction.

piecewise_up: List[float]

Array of values that characterize the update steps in upwards direction.

The values are equally spaced in w_min and w_max` (which could vary from device-to-device), where the first and the last value is set at the boundary. The update will be computed by linear interpolation of the adjacent values, depending on where the weight is currently within the range.

The values are given as relative numbers: the final update size will be computed by multiplying the value with the current dw_min of the device.

E.g. [1.5, 1, 1.5] and dw_min=0.1 means that the update (in up direction) is dw_min around zero weight value and linearly increasing to 1.5 * dw_min for larger or smaller weight values.

write_noise_std: float = 0.0

Whether to use update write noise.

Whether to use update write noise that is added to the updated devices weight, while the update is done on a hidden persistent weight. The update write noise is then sampled a new when the device is touched again.

Thus it is:

\[w_\text{apparent}{ij} = w_{ij} + \sigma_\text{write_noise}\xi\]

and the update is done on \(w_{ij}\) but the forward sees the \(w_\text{apparent}\).

class aihwkit.simulator.configs.devices.PowStepDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, pow_gamma=1.0, pow_gamma_dtod=0.1, pow_up_down=0.0, pow_up_down_dtod=0.0, write_noise_std=0.0, apply_write_noise_on_set=True)[source]

Bases: PulsedDevice

Pulsed update behavioral model: power-dependent step.

Pulsed update behavioral model, where the update step response size of the material has a power-dependent with resistance. This device model implements (a shifted from of) the Fusi & Abott (2007) synapse model (see also Frascaroli et al. (2108)).

The model based on PulsedDevice and thus shares most parameters and functionality. However, it implements new update once function, where the update step size depends in the following way. If we set \(\omega_{ij} = \frac{b_{ij}^\text{max} - w_{ij}}{b_{ij}^\text{max} - b_{ij}^\text{min}}\) the relative distance of the current weight to the upper bound, then the update per pulse is for the upwards direction:

\[w_{ij} \leftarrow w_{ij} + \Delta w_{ij}^+\,(\omega_{ij})^{\gamma_{ij}^+} \left(1 + \sigma_\text{c-to-c}\,\xi\right)\]

and in downwards direction:

\[w_{ij} \leftarrow w_{ij} + \Delta w_{ij}^-\,(1 - \omega_{ij})^{\gamma_{ij}^-} \left(1 + \sigma_\text{c-to-c}\,\xi\right)\]

Similar to \(\Delta w_{ij}^d\) the exponent \(\gamma_{ij}\) can be defined with device-to-device variation and bias in up and down direction:

\[\gamma_{ij}^d = \gamma\, \left(1 + d\, \beta_{ij} + \sigma_\text{pow-gamma-d-to-d}\xi\right)\]

where \(\xi\) is again a standard Gaussian. \(\beta_{ij}\) is the directional up versus down bias. At initialization pow_up_down_dtod and pow_up_down defines this bias term:

\[\beta_{ij} = \beta_\text{pow-up-down} + \xi\sigma_\text{pow-up-down-dtod}\]

where \(\xi\) is again a standard Gaussian number and \(\beta_\text{pow-up-down}\) corresponds to pow_up_down.

Note

The pow_gamma_dtod and pow_up_down_dtod device-to-device variation parameters are given in relative units to pow_gamma.

Note

\(\Delta w_{ij}^d\) is defined as for the PulsedDevice, however, for this device, the update step size will not be given by \(\Delta w_{ij}\) at \(w_{ij}=0\) as for most other devices models

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • pow_gamma (float) –

  • pow_gamma_dtod (float) –

  • pow_up_down (float) –

  • pow_up_down_dtod (float) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

apply_write_noise_on_set: bool = True

Whether setting the weights with set_weights will add write noise to the apparent weight state or not.

If False the persistent weight state will be equal to the apparent state initially.

bindings_class: ClassVar[Type | str | None] = 'PowStepResistiveDeviceParameter'
pow_gamma: float = 1.0

The value of \(\gamma\) as explained above.

Note

\(\gamma\) reduces essentially to the SoftBoundsDevice (if no device-to-device variation of gamma is used additionally). However, the SoftBoundsDevice will be much faster, as it does not need to compute the slow pow function.

pow_gamma_dtod: float = 0.1

Device-to-device variation for pow_gamma.

i.e. the value of \(\gamma_\text{pow-gamma-d-to-d}\) given in relative units to pow_gamma.

pow_up_down: float = 0.0

The up versus down bias of the \(\gamma\) as described above.

It is \(\gamma^+ = \gamma (1 + \beta_\text{pow-up-down})\) and \(\gamma^- = \gamma (1 - \beta_\text{pow-up-down})\) .

pow_up_down_dtod: float = 0.0

Device-to-device variation in the up versus down bias of \(\gamma\) as descibed above.

In units of pow_gamma.

write_noise_std: float = 0.0

Whether to use update write noise.

Whether to use update write noise that is added to the updated devices weight, while the update is done on a hidden persistent weight. The update write noise is then sampled a new when the device is touched again.

Thus it is:

\[w_\text{apparent}{ij} = w_{ij} + \sigma_\text{write_noise}\xi\]

and the update is done on \(w_{ij}\) but the forward sees the \(w_\text{apparent}\).

class aihwkit.simulator.configs.devices.PowStepReferenceDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, pow_gamma=1.0, pow_gamma_dtod=0.1, pow_up_down=0.0, pow_up_down_dtod=0.0, subtract_symmetry_point=False, n_estimation_steps=-1, reference_mean=0.0, reference_std=0.0)[source]

Bases: PulsedDevice

Pulsed update behavioral model: power-dependent step.

Pulsed update behavioral model, where the update step response size of the material has a power-dependent with resistance. This device model implements (a shifted from of) the Fusi & Abott (2007) synapse model (see also Frascaroli et al. (2108)).

This device model is identical to PowStepDevice, however, here it does not implement any write noise functionality but instead has an additional option to subtract a reference conductance from the weights which is statically set during initialization.

The model based on PulsedDevice and thus shares most parameters and functionality. However, it implements new update once function, where the update step size depends in the following way. If we set \(\omega_{ij} = \frac{b_{ij}^\text{max} - w_{ij}}{b_{ij}^\text{max} - b_{ij}^\text{min}}\) the relative distance of the current weight to the upper bound, then the update per pulse is for the upwards direction:

\[w_{ij} \leftarrow w_{ij} + \Delta w_{ij}^+\,(\omega_{ij})^{\gamma_{ij}^+} \left(1 + \sigma_\text{c-to-c}\,\xi\right)\]

and in downwards direction:

\[w_{ij} \leftarrow w_{ij} + \Delta w_{ij}^-\,(1 - \omega_{ij})^{\gamma_{ij}^-} \left(1 + \sigma_\text{c-to-c}\,\xi\right)\]

Similar to \(\Delta w_{ij}^d\) the exponent \(\gamma_{ij}\) can be defined with device-to-device variation and bias in up and down direction:

\[\gamma_{ij}^d = \gamma\, \left(1 + d\, \beta_{ij} + \sigma_\text{pow-gamma-d-to-d}\xi\right)\]

where \(\xi\) is again a standard Gaussian. \(\beta_{ij}\) is the directional up versus down bias. At initialization pow_up_down_dtod and pow_up_down defines this bias term:

\[\beta_{ij} = \beta_\text{pow-up-down} + \xi\sigma_\text{pow-up-down-dtod}\]

where \(\xi\) is again a standard Gaussian number and \(\beta_\text{pow-up-down}\) corresponds to pow_up_down.

Note

The pow_gamma_dtod and pow_up_down_dtod device-to-device variation parameters are given in relative units to pow_gamma.

Note

\(\Delta w_{ij}^d\) is defined as for the PulsedDevice, however, for this device, the update step size will not be given by \(\Delta w_{ij}\) at \(w_{ij}=0\) as for most other devices models

Note

In contrast to PowStepDevice, write noise is not supported.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • pow_gamma (float) –

  • pow_gamma_dtod (float) –

  • pow_up_down (float) –

  • pow_up_down_dtod (float) –

  • subtract_symmetry_point (bool) –

  • n_estimation_steps (int) –

  • reference_mean (float) –

  • reference_std (float) –

bindings_class: ClassVar[Type | str | None] = 'PowStepReferenceResistiveDeviceParameter'
n_estimation_steps: int = -1

The number of times to run an (noise-free) up / down pulse combination for the numerical estimation of the symmetry points.

In case of a non-positive number, the number of estimations steps is set 10 times the expected number of states of each device (maxed out at 10000).

pow_gamma: float = 1.0

The value of \(\gamma\) as explained above.

Note

\(\gamma\) reduces essentially to the SoftBoundsDevice (if no device-to-device variation of gamma is used additionally). However, the SoftBoundsDevice will be much faster, as it does not need to compute the slow pow function.

pow_gamma_dtod: float = 0.1

Device-to-device variation for pow_gamma.

i.e. the value of \(\gamma_\text{pow-gamma-d-to-d}\) given in relative units to pow_gamma.

pow_up_down: float = 0.0

The up versus down bias of the \(\gamma\) as described above.

It is \(\gamma^+ = \gamma (1 + \beta_\text{pow-up-down})\) and \(\gamma^- = \gamma (1 - \beta_\text{pow-up-down})\) .

pow_up_down_dtod: float = 0.0

Device-to-device variation in the up versus down bias of \(\gamma\) as descibed above.

In units of pow_gamma.

reference_mean: float = 0.0

Added to all devices of the reference \(r\).

reference_std: float = 0.0

Normal distributed device-to-device variation added to the reference \(r\).

subtract_symmetry_point: bool = False

Whether store the symmetry point of each device onto the reference device.

The symmetry point is only numerically estimated, since an analytically for is not available due to the power step model.

class aihwkit.simulator.configs.devices.PulsedDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False)[source]

Bases: _PrintableMixin

Pulsed update resistive devices.

Device are used as part of an AnalogTile to implement the update once characteristics, i.e. the material response properties when a single update pulse is given (a coincidence between row and column pulse train happened).

Common properties of all pulsed devices include:

Reset:

Resets the weight in cross points to (around) zero with cycle-to-cycle and systematic spread around a mean.

Important

Reset with given parameters is only activated when reset_weights() is called explicitly by the user.

Decay:

\[w_{ij} \leftarrow (w_{ij} - b_{ij})\,(1-\alpha_\text{decay}\delta_{ij}) + b_{ij}\]

Weight decay is only activated by inserting a specific call to decay_weights(), which is done automatically for a tile each mini-batch is decay is present. Note that the device decay_lifetime parameters (1 over decay rates \(\delta_{ij}\)) are analog tile specific and are thus set and fixed during RPU initialization. \(\alpha_\text{decay}\) is a scaling factor that can be given during run-time.

The bias \(b_{ij}\) is given by the reset bias and which is determined by the parameter reset (mean value) and reset_dtod (device-to-device variability). Thus

\[b_{ij} = \mu_\text{reset} \left(1 + \sigma_\text{reset-dtod}\xi\right)\]

Note that the reset bias is also applied in case the device is reset (see above).

Diffusion:

Similar to the decay, diffusion is only activated by inserting a specific call to diffuse_weights(), which is done automatically for a tile each mini-batch is diffusion is present. The parameters of the diffusion process are set during RPU initialization and are fixed for the remainder.

\[w_{ij} \leftarrow w_{ij} + \rho_{ij} \, \xi;\]

where \(xi\) is a standard Gaussian variable and \(\rho_{ij}\) the diffusion rate for a cross-point ij.

Note

If diffusion happens to move the weight beyond the hard bounds of the weight it is ensured to be clipped appropriately.

Drift:

Optional power-law drift setting, as described in DriftParameter.

Important

Similar to reset, drift is not applied automatically each mini-batch but requires an explicit call to drift_weights() each time the drift should be applied.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

as_bindings(data_type)[source]

Return a representation of this instance as a simulator bindings object.

Parameters:

data_type (RPUDataType) –

Return type:

Any

bindings_class: ClassVar[Type | str | None] = 'PulsedResistiveDeviceParameter'
bindings_module: ClassVar[str] = 'devices'
construction_seed: int = 0

If not equal 0, will set a unique seed for hidden parameters during construction.

corrupt_devices_prob: float = 0.0

Probability for devices to be corrupt (weights fixed to random value with hard bounds, that is min and max bounds are set to equal).

corrupt_devices_range: float = 0.1

Range around zero for establishing corrupt devices.

count_pulses: bool = False

Whether to count the positive and negative pulses that were applied.

Only for GPU devices currently implemented. Some runtime penalty expected.

Pulses can be obtained by analog_tile.tile.get_pulse_counters()

diffusion: float = 0.0

Standard deviation of diffusion process.

diffusion_dtod: float = 0.0

Device-to device variation of diffusion rate in relative units.

drift: DriftParameter

Parameter governing a power-law drift.

dw_min: float = 0.001

Mean of the minimal update step sizes across devices and directions.

dw_min_dtod: float = 0.3

Device-to-device std deviation of dw_min (in relative units to dw_min).

dw_min_dtod_log_normal: bool = False

Device-to-device std deviation dw_min_dtod given using a log-normal instead of normal distribution.

dw_min_std: float = 0.3

Cycle-to-cycle variation size of the update step (related to \(\sigma_\text{c-to-c}\) above) in relative units to dw_min.

Note

Many spread (device-to-device variation) parameters are given in relative units. For instance e.g. a setting of dw_min_std of 0.1 would mean 10% spread around the mean and thus a resulting standard deviation (\(\sigma_\text{c-to-c}\)) of dw_min * dw_min_std.

enforce_consistency: bool = True

Whether to enforce weight bounds consistency during initialization.

Whether to enforce that max weight bounds cannot be smaller than min weight bounds, and up direction step size is positive and down negative. Switches the opposite values if encountered during init.

lifetime: float = 0.0

One over decay_rate, ie \(1/r_\text{decay}\).

lifetime_dtod: float = 0.0

Device-to-device variation in the decay rate (in relative units).

perfect_bias: bool = False

No up-down differences and device-to-device variability in the bounds for the devices in the bias row.

requires_decay()[source]

Return whether device has decay enabled.

Return type:

bool

requires_diffusion()[source]

Return whether device has diffusion enabled.

Return type:

bool

reset: float = 0.0

The reset values and spread per cross-point ij when using reset functionality of the device.

reset_dtod: float = 0.0

See reset.

reset_std: float = 0.01

See reset.

up_down: float = 0.0

Up and down direction step sizes can be systematically different and also vary across devices.

\(\Delta w_{ij}^d\) is set during RPU initialization (for each cross-point \(ij\)):

\[\Delta w_{ij}^d = d\; \Delta w_\text{min}\, \left( 1 + d \beta_{ij} + \sigma_\text{d-to-d}\xi\right)\]

where \(\xi\) is again a standard Gaussian. \(\beta_{ij}\) is the directional up versus down bias. At initialization up_down_dtod and up_down defines this bias term:

\[\beta_{ij} = \beta_\text{up-down} + \xi \sigma_\text{up-down-dtod}\]

where \(\xi\) is again a standard Gaussian number and \(\beta_\text{up-down}\) corresponds to up_down. Note that up_down_dtod is again given in relative units to dw_min.

up_down_dtod: float = 0.01

See up_down.

w_max: float = 0.6

See w_min.

w_max_dtod: float = 0.3

See w_min_dtod.

w_min: float = -0.6

Mean of hard bounds across device cross-point ij.

The parameters w_min and w_max are used to set the min/max bounds independently.

Note

For this abstract device, we assume that weights can have positive and negative values and are symmetrically around zero. In physical circuit terms, this might be implemented as a difference of two resistive elements.

w_min_dtod: float = 0.3

Device-to-device variation of the hard bounds.

Device-to-device variation of the hard bounds, of min and max value, respectively. All are given in relative units to w_min, or w_max, respectively.

class aihwkit.simulator.configs.devices.SoftBoundsDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, mult_noise=True, write_noise_std=0.0, apply_write_noise_on_set=True, reverse_up=False, reverse_down=False, reverse_offset=0.01)[source]

Bases: PulsedDevice

Pulsed update behavioral model: soft bounds.

Pulsed update behavioral model, where the update step response size of the material is linearly dependent and it goes to zero at the bound.

This model is based on LinearStepDevice with parameters set to model soft bounds.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • mult_noise (bool) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

  • reverse_up (bool) –

  • reverse_down (bool) –

  • reverse_offset (float) –

apply_write_noise_on_set: bool = True

Whether setting the weights with set_weights will add write noise to the apparent weight state or not.

If False the persistent weight state will be equal to the apparent state initially.

bindings_class: ClassVar[Type | str | None] = 'SoftBoundsResistiveDeviceParameter'
mult_noise: bool = True

Whether to use multiplicative noise instead of additive cycle-to-cycle noise.

reverse_down: bool = False

Whether to increase the step size in down direction with decreasing weights (default decreases).

Note

If set, mult_noise needs to be also set.

reverse_offset: float = 0.01

Offset to add to the step size for reverse up or down to avoid zero step size at weight min or max.

reverse_up: bool = False

Whether to increase the step size in up direction with increasing weights (default decreases).

Note

If set, mult_noise needs to be also set.

write_noise_std: float = 0.0

Whether to use update write noise.

Whether to use update write noise that is added to the updated devices weight, while the update is done on a hidden persistent weight. The update write noise is then sampled anew when the device is touched again.

Thus it is:

\[w_\text{apparent}{ij} = w_{ij} + \sigma_\text{write_noise} \Delta w_\text{min}\xi\]

and the update is done on \(w_{ij}\) but the forward sees the \(w_\text{apparent}\).

class aihwkit.simulator.configs.devices.SoftBoundsPmaxDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=<factory>, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=<factory>, up_down_dtod=0.01, w_max=<factory>, w_max_dtod=0.3, w_min=<factory>, w_min_dtod=0.3, count_pulses=False, mult_noise=True, write_noise_std=0.0, apply_write_noise_on_set=True, reverse_up=False, reverse_down=False, reverse_offset=0.01, p_max=1000, alpha=0.0005, range_min=-1.0, range_max=1.0)[source]

Bases: SoftBoundsDevice

Pulsed update behavioral model: soft bounds, with a different parameterization for easier device fitting to experimental data.

Under the hood, the same device behavior as SoftboundsDevice This model is based on LinearStepDevice with parameters set to model soft bounds.

It implements pulse response function of the form:

\[ \begin{align}\begin{aligned}w(p_\text{up}) = B\left(1 -e^{-\alpha p_\text{up}} \right) + r_\text{min}\\w(p_\text{down}) = - B\left(1 - e^{-\alpha (p_\text{max} - p_\text{down})}\right) + r_\text{max}\end{aligned}\end{align} \]

where \(B=\frac{r_\text{max} - r_\text{min}}{1 - e^{-\alpha p_\text{max}}}\).

Here \(p_\text{max}\) is the number of pulses that were applied to get the device from the minimum conductance (minimum of range, \(r_\text{min}\)) to the maximum (maximum of range, \(r_\text{max}\)).

Internally the following transformation is used to get the original parameter of SoftboundsDevice:

b_factor = (range_max - range_min)/(1 - exp(-p_max * alpha))
w_min = range_min
w_max = range_min + b_factor
dw_min = b_factor * alpha
up_down = 1 + 2 * range_min / b_factor

Note

Device-to-device and cycle-to-cycle variation are defined as before (see SoftBoundsDevice, see also PulsedDevice). That is, for instance dw_min_dtod will effectively change the slope (in units of dw_min which is b_factor * alpha, see above). Range offset fluctuations can be achieved by using w_min_dtod and w_max_dtod which will vary w_min and w_max across devices, respectively.

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • mult_noise (bool) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

  • reverse_up (bool) –

  • reverse_down (bool) –

  • reverse_offset (float) –

  • p_max (int) –

  • alpha (float) –

  • range_min (float) –

  • range_max (float) –

alpha: float = 0.0005

The slope of the soft bounds model \(dw \propto \alpha w\) for both up and down direction.

as_bindings(data_type)[source]

Return a representation of this instance as a simulator bindings object.

Parameters:

data_type (RPUDataType) –

Return type:

Any

p_max: int = 1000

Number of pulses to drive the synapse from range_min to range_max.

range_max: float = 1.0

Value of the weight for \(P_max\) number of up pulses.

range_min: float = -1.0

Setting of the weight when starting the \(P_max\) up pulse experiment.

class aihwkit.simulator.configs.devices.SoftBoundsReferenceDevice(construction_seed=0, corrupt_devices_prob=0.0, corrupt_devices_range=0.1, diffusion=0.0, diffusion_dtod=0.0, drift=<factory>, dw_min=0.001, dw_min_dtod=0.3, dw_min_dtod_log_normal=False, dw_min_std=0.3, enforce_consistency=True, lifetime=0.0, lifetime_dtod=0.0, perfect_bias=False, reset=0.0, reset_dtod=0.0, reset_std=0.01, up_down=0.0, up_down_dtod=0.01, w_max=0.6, w_max_dtod=0.3, w_min=-0.6, w_min_dtod=0.3, count_pulses=False, mult_noise=False, write_noise_std=0.0, apply_write_noise_on_set=True, slope_up_dtod=0.0, slope_down_dtod=0.0, reference_mean=0.0, reference_std=0.0, subtract_symmetry_point=False)[source]

Bases: PulsedDevice

Pulsed update behavioral model: soft bounds with reference device.

Pulsed update behavioral model, where the update step response size of the material is linearly dependent.

In particular, the update behavior is

\[ \begin{align}\begin{aligned}\delta W_+ = \alpha_{+}(1 - \frac{w}{\beta_{max}}) (1 + \sigma \xi)\\\delta W_- = \alpha_{-}(1 - \frac{w}{\beta_{min}}) (1 + \sigma \xi)\end{aligned}\end{align} \]

Where the same device-to-device variation can be given as for the PulsedDevice. In addition, a device-to-device variation can be directly given on the slope. The \(\alpha_{+}\) and \(\alpha_{-}\) are the scaling factors that determine the magnitude of positive and negative weight updates.

Moreover, a fixed reference conductance can be subtracted from the resulting weight, which implemented a differential read of \(w - r\).

Parameters:

  • construction_seed (int) –

  • corrupt_devices_prob (float) –

  • corrupt_devices_range (float) –

  • diffusion (float) –

  • diffusion_dtod (float) –

  • drift (DriftParameter) –

  • dw_min (float) –

  • dw_min_dtod (float) –

  • dw_min_dtod_log_normal (bool) –

  • dw_min_std (float) –

  • enforce_consistency (bool) –

  • lifetime (float) –

  • lifetime_dtod (float) –

  • perfect_bias (bool) –

  • reset (float) –

  • reset_dtod (float) –

  • reset_std (float) –

  • up_down (float) –

  • up_down_dtod (float) –

  • w_max (float) –

  • w_max_dtod (float) –

  • w_min (float) –

  • w_min_dtod (float) –

  • count_pulses (bool) –

  • mult_noise (bool) –

  • write_noise_std (float) –

  • apply_write_noise_on_set (bool) –

  • slope_up_dtod (float) –

  • slope_down_dtod (float) –

  • reference_mean (float) –

  • reference_std (float) –

  • subtract_symmetry_point (bool) –

apply_write_noise_on_set: bool = True

Whether setting the weights with set_weights will add write noise to the apparent weight state or not.

If False the persistent weight state will be equal to the apparent state initially.

bindings_class: ClassVar[Type | str | None] = 'SoftBoundsReferenceResistiveDeviceParameter'
mult_noise: bool = False

Whether to use multiplicative noise instead of additive cycle-to-cycle noise.

reference_mean: float = 0.0

Added to all devices of the reference \(r\).

reference_std: float = 0.0

Normal distributed device-to-device variation added to the reference \(r\).

slope_down_dtod: float = 0.0

Device-to-device variation on the down-pulse slope.

Note

Since the up slope is proportional to \(\propto\frac{1}{b_\text{min}}\) the device-to-device variation of the weight max w_min_dtod will also introduce a slope variation. Turn that off, if the variation should be only on the slope directly.

slope_up_dtod: float = 0.0

Device-to-device variation on the up-pulse slope.

Note

Since the up slope is proportional to \(\propto\frac{1}{b_\text{max}}\) the device-to-device variation of the weight max w_max_dtod will also introduce a slope variation. Turn that off, if the variation should be only on the slope directly.

subtract_symmetry_point: bool = False

Whether to add the computed symmetry point of the devices onto the reference \(r\).

The symmetry point is given by:

\[w_* = \frac{\alpha_{+} - \alpha_{-}}{\frac{\alpha_{+}}{b_\text{max}} - \frac{\alpha_{-}}{b_\text{min}}}\]
write_noise_std: float = 0.0

Whether to use update write noise.

Whether to use update write noise that is added to the updated devices weight, while the update is done on a hidden persistent weight. The update write noise is then sampled anew when the device is touched again.

Thus it is:

\[w_\text{apparent}{ij} = w_{ij} + \sigma_\text{write_noise} \Delta w_\text{min}\xi\]

and the update is done on \(w_{ij}\) but the forward sees the \(w_\text{apparent}\).