agnapprox.utils.error_stats

Implementations for several probabilistic error models

Module Contents

Functions

single_dist_mc() → Tuple[float, float])	Generate error mean and standard deviation using Monte Carlo
error_prediction(→ Tuple[float, float])	Generate error mean and standard deviation using the
get_sample_population(→ numpy.ndarray)	Randomly select samples from a tensor that cover the receptive field of one neuron
population_prediction(→ Tuple[float, float])	Generate prediction of mean and standard deviation using several
to_distribution(→ Tuple[numpy.ndarray, numpy.ndarray])	Turn tensor of weights/activations into a frequency distribution (i.e. build a histogram)
error_calculation(→ Tuple[float, float])	Calculate mean and standard deviation of the observed error between

agnapprox.utils.error_stats.single_dist_mc(emap: numpy.ndarray, x_dist: numpy.ndarray, w_dist: numpy.ndarray, fan_in: float, num_samples: int = int(100000.0)) → Tuple[float, float]

Generate error mean and standard deviation using Monte Carlo approach as described in: https://arxiv.org/abs/1912.00700

Parameters:

• emap – The multiplier’s error map

• x_dist – Operand distribution of activations

• w_dist – Operand distribution of weights

• fan_in – Incoming connections for layer

• num_samples – Number of Monte Carlo simulation runs. Defaults to int(1e5).

Returns:

Mean and standard deviation for a single operation

agnapprox.utils.error_stats.error_prediction(emap: numpy.ndarray, x_dist: numpy.ndarray, w_dist: numpy.ndarray, fan_in: float) → Tuple[float, float]

Generate error mean and standard deviation using the global distribution of activations and weights

Parameters:

• emap – The multiplier’s error map

• x_dist – Operand distribution of activations

• w_dist – Operand distribution of weights

• fan_in – Incoming connections for layer

Returns:

Mean and standard deviation for a single operation

agnapprox.utils.error_stats.get_sample_population(tensor: numpy.ndarray, num_samples: int = 512) → numpy.ndarray

Randomly select samples from a tensor that cover the receptive field of one neuron

Parameters:

• tensor – Tensor to draw samples from

• num_samples – Number of samples to draw. Defaults to 512.

Returns:

Sampled 2D Tensor of shape [num_samples, tensor.shape[-1]]

agnapprox.utils.error_stats.population_prediction(emap: numpy.ndarray, x_multidist: numpy.ndarray, w_dist: numpy.ndarray, fan_in: float) → Tuple[float, float]

Generate prediction of mean and standard deviation using several sampled local distributions

Parameters:

• emap – The multiplier’s error map

• x_multidist – Array of several operand distributions for activations

• w_dist – Operand distribution of weights

• fan_in – Incoming connections for layer

Returns:

Mean and standard deviation for a single operation

agnapprox.utils.error_stats.to_distribution(tensor: Optional[numpy.ndarray], min_val: int, max_val: int) → Tuple[numpy.ndarray, numpy.ndarray]

Turn tensor of weights/activations into a frequency distribution (i.e. build a histogram)

Parameters:

• tensor – Tensor to build histogram from

• min_val – Lowest possible operand value in tensor

• max_val – Highest possible operand value in tensor

Returns:

Tuple of Arrays where first array contains the full numerical range between min_val and max_val inclusively and second array contains the relative frequency of each operand

Raises:

• ValueError – If run before features maps have been populated

• by call to utils.model.get_feature_maps –

agnapprox.utils.error_stats.error_calculation(accurate: numpy.ndarray, approximate: numpy.ndarray, fan_in: float) → Tuple[float, float]

Calculate mean and standard deviation of the observed error between accurate computation and approximate computation

Parameters:

• accurate – Accurate computation results

• approximate – Approximate computation results

• fan_in – Number of incoming neuron connections

Returns:

Mean and standard deviation for a single operation