aihwkit.inference.noise.base module
Base class for the phenomenological noise models for inference.
- class aihwkit.inference.noise.base.BaseNoiseModel(g_converter=None)[source]
Bases:
object
Base class for phenomenological noise models for inference.
- Parameters:
g_converter (BaseConductanceConverter | None) –
- apply_drift_noise(weights, drift_noise_parameters, t_inference)[source]
Apply the expected drift noise to weights.
Uses the
apply_drift_noise_to_conductances()
on each of the conductance slices.- Parameters:
weights (Tensor) – weights tensor (usually with programming noise already applied)
drift_noise_parameters (List[Tensor | None]) – list of drift nu for each conductance slice
t_inference (float) – assumed time of inference (in sec)
- Returns:
weight tensor with drift noise applied
- Return type:
Tensor
- apply_drift_noise_to_conductance(g_prog, drift_noise_param, t_inference)[source]
Apply the noise and drift up to the assumed inference time point.
- Parameters:
g_prog (Tensor) – Tensor of conductance values after programming (in \(\muS\))
drift_noise_param (Tensor | None) – typically drift nu
t_inference (float) – assumed time of inference (in sec)
- Returns:
conductance Tensor with applied noise and drift
- Return type:
Tensor
- apply_noise(weights, t_inference)[source]
Apply the expected noise.
Applies the noise to a non-perturbed conductance matrix
weights
at time of inferencet_inference
(in seconds) where 0 sec refers to the time when weight programming has finished.Note
The drift coefficients and intermediate noises etc. are sampled for each application of this function anew from the distributions, thus it samples the expected noise and drift behavior at time
t_inference
but not a continual trajectory of a given device instance over time (having e.g. constant drift coefficients).- Parameters:
weights (Tensor) –
t_inference (float) –
- Return type:
Tensor
- apply_programming_noise(weights)[source]
Apply the expected programming noise to weights.
Uses the
apply_programming_noise_to_conductances()
on each of the conductance slices.- Parameters:
weights (Tensor) – weights tensor
- Returns:
weight tensor with programming noise applied, and tuple of all drift coefficients (per conductances slice) that are determined during programming.
- Return type:
Tuple[Tensor, List[Tensor]]
- apply_programming_noise_to_conductance(g_target)[source]
Apply programming noise to a target conductance
Tensor
.- Parameters:
g_target (Tensor) – Target conductances
- Returns:
Tensor of sampled drift coefficients \(\nu\), one for each target conductance value.
- Return type:
Tensor
- generate_drift_coefficients(g_target)[source]
Generate drift coefficients.
Generate coefficients once and passed through when long-term noise and drift is applied. Typical nu_drift.
- Parameters:
g_target (Tensor) – Target conductances
- Returns:
When not overriden, it simply returns None.
- Return type:
Tensor | None