# aihwkit.simulator.configs.utils module¶

Utility parameters for resistive processing units.

class aihwkit.simulator.configs.utils.BackwardIOParameters(bm_test_negative_bound=True, bound_management=<BoundManagementType.NONE: 'None'>, inp_bound=1.0, inp_noise=0.0, inp_res=0.007936507936507936, inp_sto_round=False, is_perfect=False, max_bm_factor=1000, max_bm_res=0.25, nm_thres=0.0, noise_management=<NoiseManagementType.ABS_MAX: 'AbsMax'>, out_bound=12.0, out_noise=0.06, out_res=0.00196078431372549, out_scale=1.0, out_sto_round=False, w_noise=0.0, w_noise_type=<OutputWeightNoiseType.NONE: 'None'>)

Parameters that modify the backward IO behavior.

This class contains the same parameters as AnalogTileInputOutputParameters, specializing the default value of bound_management (as backward does not support bound management).

bound_management: aihwkit.simulator.configs.utils.BoundManagementType = 'None'

Type of noise management, see NoiseManagementType.

class aihwkit.simulator.configs.utils.BoundManagementType(value)

Bases: enum.Enum

Bound management type.

In the case Iterative the MAC is iteratively recomputed with inputs iteratively halved, when the output bound was hit.

ITERATIVE = 'Iterative'

Iteratively recomputes input scale set to $$\alpha\leftarrow\alpha/2$$.

NONE = 'None'

No bound management.

class aihwkit.simulator.configs.utils.IOParameters(bm_test_negative_bound=True, bound_management=<BoundManagementType.ITERATIVE: 'Iterative'>, inp_bound=1.0, inp_noise=0.0, inp_res=0.007936507936507936, inp_sto_round=False, is_perfect=False, max_bm_factor=1000, max_bm_res=0.25, nm_thres=0.0, noise_management=<NoiseManagementType.ABS_MAX: 'AbsMax'>, out_bound=12.0, out_noise=0.06, out_res=0.00196078431372549, out_scale=1.0, out_sto_round=False, w_noise=0.0, w_noise_type=<OutputWeightNoiseType.NONE: 'None'>)

Bases: object

Parameters that modify the IO behavior.

bm_test_negative_bound: bool = True
bound_management: aihwkit.simulator.configs.utils.BoundManagementType = 'Iterative'

Type of bound management, see BoundManagementType.

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.

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

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.

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.utils.NoiseManagementType = 'AbsMax'

Type of noise management, see NoiseManagementType.

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_res: float = 0.00196078431372549

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

out_scale: float = 1.0

out_sto_round: bool = False

Whether to enable stochastic rounding of ADC.

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.utils.OutputWeightNoiseType = '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().

class aihwkit.simulator.configs.utils.NoiseManagementType(value)

Bases: enum.Enum

Noise management type.

Noise management determines a factor $$\alpha$$ how the input is reduced:

$\mathbf{y} = \alpha\;F_\text{analog-mac}\left(\mathbf{x}/\alpha\right)$
ABS_MAX = 'AbsMax'

Use $$\alpha\equiv\max{|\mathbf{x}|}$$.

CONSTANT = 'Constant'

A constant value (given by parameter nm_thres).

MAX = 'Max'

Use $$\alpha\equiv\max{\mathbf{x}}$$.

NONE = 'None'

No noise management.

class aihwkit.simulator.configs.utils.OutputWeightNoiseType(value)

Bases: enum.Enum

Output weight noise type.

The weight noise is applied for each MAC computation, while not touching the actual weight matrix but referring it to the output.

$y_i = \sum_j w_{ij}+\xi_{ij}$
ADDITIVE_CONSTANT = 'AdditiveConstant'

The $$\xi\sim{\cal N}(0,\sigma)$$ thus all are Gaussian distributed. $$\sigma$$ is determined by w_noise.

NONE = 'None'

No weight noise.

class aihwkit.simulator.configs.utils.PulseType(value)

Bases: enum.Enum

Pulse type.

MEAN_COUNT = 'MeanCount'

Coincidence based in prob ($$p_a p_b$$).

NONE = 'None'

Floating point update instead of pulses.

NONE_WITH_DEVICE = 'NoneWithDevice'

Floating point like None, but with analog devices (e.g. weight clipping).

STOCHASTIC = 'Stochastic'

Two passes for plus and minus (only CPU).

STOCHASTIC_COMPRESSED = 'StochasticCompressed'

Generates actual stochastic bit lines. Plus and minus pulses are taken in the same pass.

class aihwkit.simulator.configs.utils.UpdateParameters(desired_bl=31, fixed_bl=True, pulse_type=<PulseType.STOCHASTIC_COMPRESSED: 'StochasticCompressed'>, res=0, sto_round=False, update_bl_management=True, update_management=True)

Bases: object

Parameter that modify the update behaviour of a pulsed device.

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 (however, see 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.utils.PulseType = 'StochasticCompressed'

Switching between different pulse types. See 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

Number of discretization steps of the probability in 0..1. Use -1 for turning discretization off. Can be $$1/n_ ext{steps}$$ as well.

sto_round: bool = False

Whether to enable stochastic rounding.

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

update_management: bool = True

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

$\gamma \equiv \max_i |x_i| / \max_j |d_j|$

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

class aihwkit.simulator.configs.utils.WeightClipParameter(fixed_value=1.0, sigma=2.5, type=<WeightClipType.NONE: 'None'>)

Bases: object

Parameter that clip the weights during hardware-aware training.

fixed_value: float = 1.0

Clipping value in case of FixedValue type.

sigma: float = 2.5

Sigma value for clipping for the LayerGaussian type.

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

Type of clipping.

class aihwkit.simulator.configs.utils.WeightClipType(value)

Bases: enum.Enum

Weight clipper type.

AVERAGE_CHANNEL_MAX = 'AverageChannelMax'

Calculates the abs max of each output channel (row of the weight matrix) and takes the average as clipping value for all.

FIXED_VALUE = 'FixedValue'

Clip to fixed value give, symmetrical around zero.

LAYER_GAUSSIAN = 'LayerGaussian'

Calculates the second moment of the whole weight matrix and clips at sigma times the result symmetrically around zero.

NONE = 'None'

None.

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, type=<WeightModifierType.COPY: 'Copy'>)

Bases: object

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

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.

dorefa_clip: float = 0.6

Parameter for DoReFa.

enable_during_test: bool = False

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.utils.WeightModifierType = 'Copy'

Type of the weight modification.

class aihwkit.simulator.configs.utils.WeightModifierType(value)

Bases: enum.Enum

Weight modifier type.

ADD_NORMAL = 'AddNormal'

COPY = 'Copy'

Just copy, however, could also drop.

DISCRETIZE = 'Discretize'

Quantize the weights.

DISCRETIZE_ADD_NORMAL = 'DiscretizeAddNormal'

First discretize and then additive Gaussian noise.

DOREFA = 'DoReFa'

DoReFa discretization.

MULT_NORMAL = 'MultNormal'

Mutiplicative Gaussian noise.

aihwkit.simulator.configs.utils.parameters_to_bindings(params)

Convert a dataclass parameter into a bindings class.

Parameters

params (Any) –

Return type

Any

aihwkit.simulator.configs.utils.tile_parameters_to_bindings`(params)

Convert a tile dataclass parameter into a bindings class.

Parameters

params (Any) –

Return type

Any