agnapprox.utils.select_multipliers

Utility functions to select approximate multipliers based on reference data

Module Contents

Classes

LayerInfo	Multiplier Matching result for a single layer
MatchingInfo	Multiplier Matching result for the entire model

Functions

select_layer_multiplier(→ Tuple[str, float])	Select a matching approximate multiplier for a single layer
deploy_multipliers(model, matching_result, library)	Deploy selected approximate multipliers to network
select_multipliers(→ MatchingInfo)	Select matching Approximate Multipliers for all layers in a model

Attributes

logger

agnapprox.utils.select_multipliers.logger

agnapprox.utils.select_multipliers.select_layer_multiplier(intermediate_results: agnapprox.utils.model.IntermediateLayerResults, multipliers: List[evoapproxlib.ApproximateMultiplier], max_noise: float, num_samples: int = 512) → Tuple[str, float]

Select a matching approximate multiplier for a single layer

Parameters:

• layer_ref_data – Reference input/output data generated from a model run with accurate multiplication. This is used to calibrate the layer standard deviation for the error estimate and determine the distribution of numerical values in weights and activations.

• multipliers – Approximate Multiplier Error Maps, Performance Metrics and name

• max_noise – Learned allowable noise parameter (sigma_l)

• num_samples – Number of samples to draw from features for multi-population prediction. Defaults to 512.

Returns:

Dictionary with name and performance metric of selected multiplier

class agnapprox.utils.select_multipliers.LayerInfo

Multiplier Matching result for a single layer

name :str

multiplier_name :str

multiplier_performance_metric :float

opcount :float

relative_opcount(total_opcount: float)

Calculate the relative contribution of this layer to the network’s total operations

Parameters:

total_opcount – Number of operations in the entire networks

Returns:

• 0: layer contributes no operations to the network’s opcount

• 1: layer conttibutes all operations to the network’s opcount

Return type:

float between 0..1 where

relative_energy_consumption(metric_max: float)

Relative energy consumption of selected approximate multiplier

Parameters:

metric_max – Highest possible value for performance metric (typically that of the respective accurate multiplier)

Returns:

• 0: selected multiplier consumes no energy

• 1: selected multiplier consumes the maximum amount of energy

Return type:

float between 0..1 where

class agnapprox.utils.select_multipliers.MatchingInfo

Multiplier Matching result for the entire model

layers :List[LayerInfo]

metric_max :float

opcount :float

property relative_energy_consumption

Relative Energy Consumption compared to network without approximation achieved by the current AM configuration

Returns:

sum relative energy consumption for each layer, weighted with the layer’s contribution to overall operations

agnapprox.utils.select_multipliers.deploy_multipliers(model: agnapprox.nets.ApproxNet, matching_result: MatchingInfo, library)

Deploy selected approximate multipliers to network

Parameters:

• model – Model to deploy multipliers to

• matching_result – Results of multiplier matching

• library – Library to load Lookup tables from

agnapprox.utils.select_multipliers.select_multipliers(model: agnapprox.nets.ApproxNet, datamodule: pytorch_lightning.LightningDataModule, multipliers: List[evoapproxlib.ApproximateMultiplier], trainer: pytorch_lightning.Trainer) → MatchingInfo

Select matching Approximate Multipliers for all layers in a model

Parameters:

• model – Approximate Model with learned layer robustness parameters

• datamodule – Data Module to use for sampling runs

• library – Approximate Multiplier Library provider

• trainer – PyTorch Lightning Trainer instance to use for sampling run

• signed – Whether to select signed or unsigned instances from Multiplier library provide. Defaults to True.

Returns:

Dictionary of Assignment results