aihwkit.inference.noise.reram module

Phenomenological noise models for ReRAM devices for inference.

class aihwkit.inference.noise.reram.ReRamWan2022NoiseModel(coeff_dic=None, g_converter=None, g_max=None, noise_scale=1.0, coeff_g_max_reference=None)[source]

Bases: BaseNoiseModel

Noise model that was inferred from ReRam publication data.

This ReRam model is and approximation to the data published by Wan et al. Nature (2022).

Conductance dependence of the deviations from the target conductance was estimated from the published figures and fitted with a 4-th order polynomial (only 1 sec, 1 day, 2 day).

No separate data is available for read noise (1/f).

Note

To account for short-term read noise (about 1%) one should additional set the forward.w_noise parameter to about 0.01 (with w_noise_type=WeightNoiseType.ADDITIVE_CONSTANT)

Parameters:

  • coeff_dic (Dict[float, List] | None) – polynomial coefficients in \(\mu S\), \(\sum_i c_i \left(\frac{g_t}{g_\max}\right)^i\) for each time. If not given, the fitted measurement is taken at selected time points only

  • g_converter (BaseConductanceConverter | None) – Instantiated class of the conductance converter (defaults to single pair).

  • g_max (float | None) – In \(\mu S\), the maximal conductance, i.e. the value the absolute max of the weights will be mapped to.

  • noise_scale (float) – Additional scale for the noise.

  • coeff_g_max_reference (float | None) – reference \(g_\max\) value when fitting the coefficients, since the result of the polynomial fit is given in uS. If coeff_g_max_reference is not given and coeffs are given explicitely, it will be set to g_max of the conductance converter.

apply_drift_noise_to_conductance(g_prog, g_target, t_inference)[source]

Apply the accumulated noise according to the time of inference.

Will use unique 4th-order polynomial fit to the ReRAM measurements to the target values.

Parameters:

  • g_prog (Tensor) – will be ignored

  • g_target (Tensor) – target conductance values that will be used to add noise

  • t_inference (float) – time of inference.

Returns:

conductances with noise applied

Raises:

ArgumentError – if t_inference is not one of (1, 24*3600, 2*24*3600) seconds (or any user-defined key in coeff_dic), the error will be raised.

Return type:

Tensor

apply_programming_noise_to_conductance(g_target)[source]

Apply programming noise to a target conductance Tensor.

Programming noise with additive Gaussian noise with conductance dependency of the variance given by a 2-degree polynomial.

Parameters:

g_target (Tensor) –

Return type:

Tensor

generate_drift_coefficients(g_target)[source]

Return target values as coefficients.

Since ReRAM does not show drift in the usual sense, here simply the target values will given as coefficients to compute the long-term variations on-the-fly

Parameters:

g_target (Tensor) –

Return type:

Tensor