aihwkit.simulator.configs.utils module

Utility parameters for resistive processing units.

class aihwkit.simulator.configs.utils.DriftParameter(nu=0.0, t_0=1.0, reset_tol=1e-07, nu_dtod=0.0, nu_std=0.0, wg_ratio=1.0, g_offset=0.0, w_offset=0.0, nu_k=0.0, log_g0=0.0, w_noise_std=0.0)

Bases: aihwkit.simulator.configs.utils.SimpleDriftParameter

Parameter for a power law drift.

The drift is based on the model described by Oh et al (2019).

It computes: .. math:

w_{ij}*\left(\frac{t + \Delta t}{t_0}\right)^(-\nu^\text{actual}_{ij})

where the drift coefficient is drawn once at the beginning and might depend on device. It also can depend on the actual weight value.

The actual drift coefficient is computed as: .. math:

\nu_{ij}^\text{actual} =  \nu_{ij} - \nu_k \log \frac{(w_{ij} - w_\text{off}) / r_\text{wg}
+ g_\text{off}}{G_0}  + \nu\sigma_\nu\xi

here \(w_{ij}\) is the actual weight and nu_{ij} fixed for each device given by the mean \(\nu\) and the device-to-device variation: \(\nu_{ij} = \nu + \nu_dtod\nu\xi\) and are only drawn once at the beginning (tile instantiation). xi is Gaussian noise.

Note

If the weight has changed from the last drift call (determined by the reset_tol parameter), for instance due to update, decay or noise, then the drift time \(t\) will be reset and start from new, however, the drift coefficients \(\nu_{ij}\) are not changed. On the other hand, if the weights has not changed since last call, \(t\) will accumulate the time.

Caution

Note that the drift coefficient does not depend on the initially programmed weight value at \(t=0\) in the current implementation (ie G0 is a constant for all devices), but instead on the actual weight. In some materials (e.g. phase changed materials), that might be not accurate.

Parameters

• nu (float) –

• t_0 (float) –

• reset_tol (float) –

• nu_dtod (float) –

• nu_std (float) –

• wg_ratio (float) –

• g_offset (float) –

• w_offset (float) –

• nu_k (float) –

• log_g0 (float) –

• w_noise_std (float) –

Return type

None

g_offset: float = 0.0

g_min to convert to physical units.

log_g0: float = 0.0

Log g0.

nu_dtod: float = 0.0

Device-to-device variation of the \(\nu\) values.

nu_k: float = 0.0

nu with \(W\).

That is \(\nu(R) = nu_0 - k \log(G/G_0)\). See Oh et al. for details.

Type

Variation of math

nu_std: float = 0.0

Cycle-to-cycle variation of \(\nu\).

A more realistic way to add noise of the drift might be using w_noise_std.

w_noise_std: float = 0.0

Additional weight noise (Gaussian diffusion) added to the weights after the drift is applied.

w_offset: float = 0.0

w(g_min), i.e. to what value g_min is mapped to in w-space.

wg_ratio: float = 1.0

(w_max-w_min)/(g_max-g_min) to convert to physical units.

class aihwkit.simulator.configs.utils.IOParameters(is_perfect=False, mv_type=AnalogMVType.ONE_PASS, inp_bound=1.0, inp_noise=0.0, inp_res=0.007936507936507936, inp_sto_round=False, inp_asymmetry=0.0, out_bound=12.0, out_noise=0.06, out_res=0.00196078431372549, out_sto_round=False, out_scale=1.0, out_asymmetry=0.0, bound_management=BoundManagementType.ITERATIVE, noise_management=NoiseManagementType.ABS_MAX, w_noise=0.0, w_noise_type=WeightNoiseType.NONE, ir_drop=0.0, ir_drop_g_ratio=571428.5714285714, out_nonlinearity=0.0, out_nonlinearity_std=0.0, slope_calibration=0.0, v_offset_std=0.0, v_offset_w_min=- 1.0, r_series=0.0, w_read_asymmetry_dtod=0.0, max_bm_factor=1000, max_bm_res=0.25, bm_test_negative_bound=True, nm_thres=0.0)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter that define the analog-matvec (forward / backward) and peripheral digital input-output behavior.

Here one can enable analog-digital conversion, dynamic input scaling, and define the properties of the analog-matvec computations, such as noise and non-idealities (e.g. IR-drop).

Parameters

• is_perfect (bool) –

• mv_type (aihwkit.simulator.configs.enums.AnalogMVType) –

• inp_bound (float) –

• inp_noise (float) –

• inp_res (float) –

• inp_sto_round (bool) –

• inp_asymmetry (float) –

• out_bound (float) –

• out_noise (float) –

• out_res (float) –

• out_sto_round (bool) –

• out_scale (float) –

• out_asymmetry (float) –

• bound_management (aihwkit.simulator.configs.enums.BoundManagementType) –

• noise_management (aihwkit.simulator.configs.enums.NoiseManagementType) –

• w_noise (float) –

• w_noise_type (aihwkit.simulator.configs.enums.WeightNoiseType) –

• ir_drop (float) –

• ir_drop_g_ratio (float) –

• out_nonlinearity (float) –

• out_nonlinearity_std (float) –

• slope_calibration (float) –

• v_offset_std (float) –

• v_offset_w_min (float) –

• r_series (float) –

• w_read_asymmetry_dtod (float) –

• max_bm_factor (int) –

• max_bm_res (float) –

• bm_test_negative_bound (bool) –

• nm_thres (float) –

Return type

None

bm_test_negative_bound: bool = True

bound_management: aihwkit.simulator.configs.enums.BoundManagementType = 'Iterative'

Type of bound management, see BoundManagementType.

Caution

Bound management is only available for the forward pass. It will be ignored when used for the backward pass.

inp_asymmetry: float = 0.0

Input asymmetry \(a_ ext{input}\).

Input of the negative input pass is scaled by \((1 - a_ ext{input})\).

Note

This setting has only effect in case of and AnalogMVType that uses separate passes for positive and negative inputs.

inp_bound: float = 1.0

Input bound and ranges for the digital-to-analog converter (DAC).

inp_noise: float = 0.0

Std deviation of Gaussian input noise (\(\sigma_\text{inp}\)).

i.e. noisiness of the analog input (at the stage after DAC and before the multiplication).

inp_res: float = 0.007936507936507936

Number of discretization steps for DAC (\(\le0\) means infinite steps) or resolution (1/steps).

inp_sto_round: bool = False

Whether to enable stochastic rounding of DAC.

ir_drop: float = 0.0

Scale of IR drop along the inputs (rows of the weight matrix).

The IR-drop is calculated assuming that the first input is farthest away from the output channel. The expected drop is approximating the steady-state voltage distributions and depends on the input current.

ir_drop_g_ratio: float = 571428.5714285714

Physical ratio of wire conductance from one cell to the next to physical max conductance of a device.

Default is compute with 5mS maximal conductance set state and 0.35 Ohm wire resistance.

is_perfect: bool = False

Short-cut to compute a perfect forward pass.

If True, it assumes an ideal forward pass (e.g. no bound, ADC etc…). Will disregard all other settings in this case.

max_bm_factor: int = 1000

Maximal bound management factor.

If this factor is reached then the iterative process is stopped.

max_bm_res: float = 0.25

Limit the maximal number of iterations of the bound management.

Another way to limit the maximal number of iterations of the bound management. The max effective resolution number of the inputs, e.g. use \(1/4\) for 2 bits.

mv_type: aihwkit.simulator.configs.enums.AnalogMVType = 'OnePass'

Selects the type of analog mat-vec computation. See AnalogMVType for details.

nm_thres: float = 0.0

Constant noise management value for type Constant.

In other cases, this is a upper threshold \(\theta\) above which the noise management factor is saturated. E.g. for AbsMax:

\begin{equation*} \alpha=\begin{cases}\max_i|x_i|, & \text{if} \max_i|x_i|<\theta \\ \theta, & \text{otherwise}\end{cases} \end{equation*}

Caution

If nm_thres is set (and type is not Constant), the noise management will clip some large input values, in favor of having a better SNR for smaller input values.

noise_management: aihwkit.simulator.configs.enums.NoiseManagementType = 'AbsMax'

Type of noise management, see NoiseManagementType.

out_asymmetry: float = 0.0

Output asymmetry \(a_ ext{output}\).

Output of the negative input pass is scaled by \((1 - a_ ext{output})\).

Note

This setting has only effect in case of and AnalogMVType that uses separate passes for positive and negative inputs.

out_bound: float = 12.0

Output bound and ranges for analog-to-digital converter (ADC).

out_noise: float = 0.06

Std deviation of Gaussian output noise (\(\sigma_\text{out}\)).

i.e. noisiness of device summation at the output.

out_nonlinearity: float = 0.0

S-shaped non-linearity applied to the analog output.

Output non-linearity applies an S-shaped non-linearity to the analog output (before the ADC), i.e. :math:` rac{y_i}{1 + n_i*|y_i|}` where \(n_i\) is drawn at the instantiation time by:

out_nonlinearity / out_bound * (1 + out_nonlinearity_std * rand)

out_nonlinearity_std: float = 0.0

Output-to-output non linearity variation.

out_res: float = 0.00196078431372549

Number of discretization steps for ADC or resolution.

Number of discretization steps for ADC (\(<=0\) means infinite steps) or resolution (1/steps).

out_scale: float = 1.0

Additional fixed scalar factor.

out_sto_round: bool = False

Whether to enable stochastic rounding of ADC.

r_series: float = 0.0

Series resistance in fraction of the total output current.

slope_calibration: float = 0.0

Models a calibration process of the output non-linearity (and r-series).

This is the relative value in the output range where the slope of the non-linearity should have slope 1. E.g. 0.5 would be at half-out range.

v_offset_std: float = 0.0

Voltage offset variation.

The output is multiplied by a systematic factor set for each output line at time of instantiation, e.g. \((1 - v_i)\) for the coding device and \((1 + v_i)\) for the reference device (assuming differential reads).

v_offset_w_min: float = -1.0

Voltage offset for an implicit reference unit.

w_noise: float = 0.0

Scale of output referred weight noise (\(\sigma_w\)) for a given w_noise_type.

w_noise_type: aihwkit.simulator.configs.enums.WeightNoiseType = 'None'

Type as specified in OutputWeightNoiseType.

Note

This noise us applied each time anew as it is referred to the output. It will not change the conductance values of the weight matrix. For the latter one can apply diffuse_weights().

w_read_asymmetry_dtod: float = 0.0

Device polarity read dependence.

The negative inputs perceive a slightly different weight (e.g. pcm polarity dependence). Each device has a different factor, and the spread of this device-to-device variability can be set with w_read_asymmetry_dtod. A weight (given negative input) will be then scaled by \(1 - f_{ij}\) where \(f_{ij}\) is drawn from a Gaussian distribution (with zero mean and standard deviation w_read_asymmetry_dtod).

class aihwkit.simulator.configs.utils.InputRangeParameter(enable=False, init_value=3.0, init_from_data=100, init_std_alpha=3.0, decay=0.001, input_min_percentage=0.95, manage_output_clipping=True, output_min_percentage=0.95, gradient_scale=1.0, gradient_relative=True)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter related to input range learning

Parameters

• enable (bool) –

• init_value (float) –

• init_from_data (int) –

• init_std_alpha (float) –

• decay (float) –

• input_min_percentage (float) –

• manage_output_clipping (bool) –

• output_min_percentage (float) –

• gradient_scale (float) –

• gradient_relative (bool) –

Return type

None

decay: float = 0.001

Decay rate for input range learning.

enable: bool = False

Whether to enable to learn the input range. Note that if enable is False then no clip is applied.

Note

The input bound (forward.inp_bound) is assumed to be 1 if enabled as the input range already scales the input into to the range \((-1, 1)\) by dividing the input to the type by itself and multiplying the output accordingly.

Typically, noise and bound management should be set to NONE for the input range learning as it replaces the dynamic managements with a static but learned input bound. However, in some exceptional experimental cases one might want to enable the management techniques on top of the input range learning, so that no error is raised if they are not set to NONE.

gradient_relative: bool = True

Whether to make the gradient of the input range learning relative to the current range value.

gradient_scale: float = 1.0

Scale of the gradient magnitude (learning rate) for the input range learning.

init_from_data: int = 100

Number of batches to use for initialization from data. Set 0 to turn off.

init_std_alpha: float = 3.0

Standard deviation multiplier for initialization from data.

init_value: float = 3.0

Initial setting of the input range in case of input range learning.

input_min_percentage: float = 0.95

Decay is only applied if percentage of non-clipped values is above this value.

Note

The added gradient is (in case of non-clipped input percentage percentage > input_min_percentage):

grad += decay * input_range

manage_output_clipping: bool = True

Whether to increase the input range when output clipping occurs.

Caution

The output bound is taken from the forward.out_bound value, which has to exist. Noise and bound management have to be set to NONE if this feature is enabled otherwise a ConfigError is raised.

output_min_percentage: float = 0.95

Increase of the input range is only applied if percentage of non-clipped output values is below this value.

Note

The gradient subtracted from the input range is (in case of output_percentage < output_min_percentage):

grad -= (1.0 - output_percentage) * input_range

supports_manage_output_clipping(rpu_config)

Checks whether rpu_config supported manage_output_clipping.

Parameters

rpu_config (Any) – RPUConfig to check

Returns

True if supported otherwise False

Return type

bool

class aihwkit.simulator.configs.utils.MapableRPU(mapping=<factory>)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Defines the mapping parameters and utility factories

Parameters

mapping (aihwkit.simulator.configs.utils.MappingParameter) –

Return type

None

get_linear()

Returns a AnalogLinear module as specified

Return type

Union[Type[AnalogLinear], Type[AnalogLinearMapped]]

mapping: aihwkit.simulator.configs.utils.MappingParameter

Parameter related to mapping weights to tiles for supporting modules.

class aihwkit.simulator.configs.utils.MappingParameter(digital_bias=True, weight_scaling_omega=0.0, weight_scaling_columnwise=False, learn_out_scaling=False, out_scaling_columnwise=False, max_input_size=512, max_output_size=512)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter related to hardware design and the mapping of logical weight matrices to physical tiles.

Caution

Some of these parameters have only an effect for modules that support tile mappings.

Parameters

• digital_bias (bool) –

• weight_scaling_omega (float) –

• weight_scaling_columnwise (bool) –

• learn_out_scaling (bool) –

• out_scaling_columnwise (bool) –

• max_input_size (int) –

• max_output_size (int) –

Return type

None

digital_bias: bool = True

Whether the bias term is handled by the analog tile or kept in digital.

Note

Default is having a digital bias so that bias values are not stored onto the analog crossbar. This needs to be supported by the chip design. Set to False if the analog bias is instead situated on the the crossbar itself (as an extra column)

Note

digital_bias is supported by all analog modules.

learn_out_scaling: bool = False

Define (additional) out scales that are learnable parameter used to scale the output.

max_input_size: int = 512

Maximal input size (number of columns) of the weight matrix that is handled on a single analog tile.

If the logical weight matrix size exceeds this size it will be split and mapped onto multiple analog tiles.

Caution

Only relevant for Mapped modules such as aihwkit.nn.modules.linear_mapped.AnalogLinearMapped.

max_output_size: int = 512

Maximal output size (number of rows) of the weight matrix that is handled on a single analog tile.

If the logical weight matrix size exceeds this size it will be split and mapped onto multiple analog tiles.

Caution

Only relevant for Mapped modules such as aihwkit.nn.modules.linear_mapped.AnalogLinearMapped.

out_scaling_columnwise: bool = False

Whether the learnable out scaling parameter enabled by learn_out_scaling is a scalar (False) or learned for each output (True).

weight_scaling_columnwise: bool = False

Whether the weight matrix will be remapped column-wise over the maximum device allowed value.

weight_scaling_omega: float = 0.0

omega_scale is a user defined parameter used to scale the weights while remapping these to cover the full range of values allowed.

class aihwkit.simulator.configs.utils.PrePostProcessingParameter(input_range=<factory>)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter related to digital input and output processing, such as input clip learning.

Parameters

input_range (aihwkit.simulator.configs.utils.InputRangeParameter) –

Return type

None

input_range: aihwkit.simulator.configs.utils.InputRangeParameter

class aihwkit.simulator.configs.utils.PrePostProcessingRPU(pre_post=<factory>)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Defines the pre-post parameters and utility factories

Parameters

pre_post (aihwkit.simulator.configs.utils.PrePostProcessingParameter) –

Return type

None

pre_post: aihwkit.simulator.configs.utils.PrePostProcessingParameter

Parameter related digital pre and post processing.

class aihwkit.simulator.configs.utils.SimpleDriftParameter(nu=0.0, t_0=1.0, reset_tol=1e-07)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter for a simple power law drift.

The drift as a simple power law drift without device-to-device variation or conductance dependence.

It computes: .. math:

w_{ij}*\left(\frac{t + \Delta t}{t_0}\right)^(-\nu)

Parameters

• nu (float) –

• t_0 (float) –

• reset_tol (float) –

Return type

None

nu: float = 0.0

Average drift \(\nu\) value.

Need to non-zero to actually use the drift.

reset_tol: float = 1e-07

Reset tolerance.

This should a number smaller than the expected weight change as it is used to detect any changes in the weight from the last drift call. Every change to the weight above this tolerance will reset the drift time.

Caution

Any write noise or diffusion on the weight might thus interfere with the drift.

t_0: float = 1.0

Time between write and first read.

Usually assumed in milliseconds, however, it really determines the time units of time_since_last_call when calling the drift.

class aihwkit.simulator.configs.utils.UpdateParameters(desired_bl=31, fixed_bl=True, pulse_type=PulseType.STOCHASTIC_COMPRESSED, res=0, x_res_implicit=0, d_res_implicit=0, sto_round=False, update_bl_management=True, update_management=True, um_grad_scale=1.0)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter that modify the update behaviour of a pulsed device.

Parameters

• desired_bl (int) –

• fixed_bl (bool) –

• pulse_type (aihwkit.simulator.configs.enums.PulseType) –

• res (float) –

• x_res_implicit (float) –

• d_res_implicit (float) –

• sto_round (bool) –

• update_bl_management (bool) –

• update_management (bool) –

• um_grad_scale (float) –

Return type

None

d_res_implicit: float = 0

Resolution of each quantization step for the error d.

Resolution (ie. bin width) of each quantization step for the error d in case of DeterministicImplicit pulse trains. See PulseTypeMap for details.

desired_bl: int = 31

Desired length of the pulse trains.

For update BL management, it is the maximal pulse train length.

fixed_bl: bool = True

Whether to fix the length of the pulse trains.

See also update_bl_management.

In case of True (where dw_min is the mean minimal weight change step size) it is:

BL = desired_BL
A = B =  sqrt(learning_rate / (dw_min * BL))

In case of False:

if dw_min * desired_BL < learning_rate:
    A = B = 1
    BL = ceil(learning_rate / dw_min
else:
    # same as for fixed_BL=True

pulse_type: aihwkit.simulator.configs.enums.PulseType = 'StochasticCompressed'

Switching between different pulse types.

See also PulseTypeMap for details.

Important

Pulsing can also be turned off in which case the update is done as if in floating point and all other update related parameter are ignored.

res: float = 0

Resolution of the update probability for the stochastic bit line generation.

Resolution ie. bin width in 0..1) of the update probability for the stochastic bit line generation. Use -1 for turning discretization off. Can be given as number of steps as well.

sto_round: bool = False

Whether to enable stochastic rounding.

um_grad_scale: float = 1.0

Scales the gradient for the update management.

The factor \(\alpha\) for the update_management. If smaller than 1 it means that the gradient will be earlier clipped when learning rate is too large (ie. exceeding the maximal pulse number times the weight granularity). If 1, both d and x inputs are clipped for the same learning rate.

update_bl_management: bool = True

Whether to enable dynamical adjustment of A,``B``,and BL:

BL = ceil(learning_rate * abs(x_j) * abs(d_i) / weight_granularity);
BL  = min(BL,desired_BL);
A = B = sqrt(learning_rate / (weight_granularity * BL));

The weight_granularity is usually equal to dw_min.

update_management: bool = True

Whether to apply additional scaling.

After the above setting an additional scaling (always on when using update_bl_management`) is applied to account for the different input strengths. If

\[\gamma \equiv \max_i |x_i| / (\alpha \max_j |d_j|)\]

is the ratio between the two maximal inputs, then A is additionally scaled by \(\gamma\) and B is scaled by \(1/\gamma\).

The gradient scale \(\alpha\) can be set with um_grad_scale

x_res_implicit: float = 0

Resolution of each quantization step for the inputs x.

Resolution (ie. bin width) of each quantization step for the inputs x in case of DeterministicImplicit pulse trains. See PulseTypeMap for details.

class aihwkit.simulator.configs.utils.WeightClipParameter(fixed_value=- 1.0, sigma=2.5, type=WeightClipType.NONE)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter that clip the weights during hardware-aware training.

Important

A clipping type has to be set before any of the parameter changes take any effect.

Parameters

• fixed_value (float) –

• sigma (float) –

• type (aihwkit.simulator.configs.enums.WeightClipType) –

Return type

None

fixed_value: float = -1.0

Clipping value in case of FixedValue type.

Caution

If fixed_value > 0 it will be also applied during other clipping types.

sigma: float = 2.5

Sigma value for clipping for the LayerGaussian type.

type: aihwkit.simulator.configs.enums.WeightClipType = 'None'

Type of clipping.

class aihwkit.simulator.configs.utils.WeightModifierParameter(std_dev=0.0, res=0.0, sto_round=False, dorefa_clip=0.6, pdrop=0.0, enable_during_test=False, rel_to_actual_wmax=True, assumed_wmax=1.0, copy_last_column=False, coeffs=<factory>, type=WeightModifierType.COPY)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter that modify the forward/backward weights during hardware-aware training.

Parameters

• std_dev (float) –

• res (float) –

• sto_round (bool) –

• dorefa_clip (float) –

• pdrop (float) –

• enable_during_test (bool) –

• rel_to_actual_wmax (bool) –

• assumed_wmax (float) –

• copy_last_column (bool) –

• coeffs (List[float]) –

• type (aihwkit.simulator.configs.enums.WeightModifierType) –

Return type

None

assumed_wmax: float = 1.0

Assumed weight value that is mapped to the maximal conductance.

This is typically 1.0. This parameter will be ignored if rel_to_actual_wmax is set.

coeffs: List[float]

Coefficients for the POLY weight modifier type.

See WeightModifierType for details.

copy_last_column: bool = False

Whether to not apply noise to the last column (which usually contains the bias values).

dorefa_clip: float = 0.6

Parameter for DoReFa.

enable_during_test: bool = False

Whether to use the last modified weight matrix during testing.

Caution

This will not remove drop connect or any other noise during evaluation, and thus should only used with care.

pdrop: float = 0.0

Drop connect probability.

Drop connect sets weights to zero with the given probability. This implements drop connect.

Important

Drop connect can be used with any other modifier type in combination.

rel_to_actual_wmax: bool = True

Whether to calculate the abs max of the weight and apply noise relative to this number.

If set to False, assumed_wmax is taken as relative units.

res: float = 0.0

Resolution of the discretization.

The invert of res gives the number of equal sized steps in \(-a_\text{max}\ldots,a_\text{max}\) where the \(a_\text{max}\) is either given by the abs max (if rel_to_actual_wmax is set) or assumed_wmax otherwise.

res is only used in the modifier types DoReFa, Discretize, and DiscretizeAddNormal.

std_dev: float = 0.0

Standard deviation of the added noise to the weight matrix.

This parameter affects the modifier types AddNormal, MultNormal and DiscretizeAddNormal.

Note

If the parameter rel_to_actual_wmax is set then the std_dev is computed in relative terms to the abs max of the given weight matrix, otherwise it in relative terms to the assumed max, which is set by assumed_wmax.

sto_round: bool = False

Whether the discretization is done with stochastic rounding enabled.

sto_round is only used in the modifier types DoReFa, Discretize, and DiscretizeAddNormal.

type: aihwkit.simulator.configs.enums.WeightModifierType = 'Copy'

Type of the weight modification.

class aihwkit.simulator.configs.utils.WeightRemapParameter(remapped_wmax=1.0, max_scale_range=0.0, max_scale_ref=0.0, type=WeightRemapType.NONE)

Bases: aihwkit.simulator.configs.helpers._PrintableMixin

Parameter that remap the weights during hardware-aware training.

Important

A remap type has to be set before any of the parameter changes take any effect.

Parameters

• remapped_wmax (float) –

• max_scale_range (float) –

• max_scale_ref (float) –

• type (aihwkit.simulator.configs.enums.WeightRemapType) –

Return type

None

max_scale_range: float = 0.0

Maximal range of scale values. Use zero to turn any restrictions off (default).

max_scale_ref: float = 0.0

Reference scale that use used as minimal scale for determining the scale range.

remapped_wmax: float = 1.0

Assumed max of weight, ie the value of the weight the maximal conductance is mapped to. Typically 1.0.

type: aihwkit.simulator.configs.enums.WeightRemapType = 'None'

Type of clipping.