devices.xylo.syns61201.AFESamna

class devices.xylo.syns61201.AFESamna(*args, **kwargs)

Bases: Module

Interface to the Audio Front-End module on a Xylo-A2 HDK

This module uses samna to interface to the AFE hardware on a Xylo-A2 HDK. It permits recording from the AFE hardware.

To record from the module, use the evolve() method. You need to pass this method an empty matrix, with the desired number of time-steps. The time-step dt is specified at module instantiation.

A simulation of the module is available in AFESim.

Warning

This module does not currently support manual configuration. A fixed configuration is provided which uses auto-calibration, applied when the module is instantiated. This takes approximately 50 seconds to configure, leading to slow instantiation.

See also

For information about the Audio Front-End design, and examples of using AFESim for a simulation of the AFE, see Using the analog frontend model.

Examples

Instantiate an AFE module, connected to a Xylo-A2 HDK

>>> from rockpool.devices.xylo import AFESamna
>>> import rockpool.devices.xylo.syns61201.xa2_devkit_utils as xdu
>>> afe_hdks = xdu.find_xylo_a2_boards()
>>> afe = AFESamna(afe_hdks[0], dt = 10e-3)

Use the module to record some audio events

>>> import numpy as np
>>> audio_events = afe(np.zeros([0, 100, 0]))

Attributes overview

class_name	Class name of self
full_name	The full name of this module (class plus module name)
name	The name of this module, or an empty string if None
shape	The shape of this module
size	(DEPRECATED) The output size of this module
size_in	The input size of this module
size_out	The output size of this module
spiking_input	If True, this module receives spiking input.
spiking_output	If True, this module sends spiking output.

Methods overview

__init__(device[, config, dt, ...])	Instantiate an AFE module, via a samna backend
as_graph()	Convert this module to a computational graph
attributes_named(name)	Search for attributes of this or submodules by time
evolve(input_data[, record])	Use the AFE HW module to record live audio and return as encoded events
modules()	Return a dictionary of all sub-modules of this module
parameters([family])	Return a nested dictionary of module and submodule Parameters
reset_parameters()	Reset all parameters in this module
reset_state()	Reset the state of this module
save_config(filename)	Save an AFE configuration to disk in JSON format
set_attributes(new_attributes)	Set the attributes and sub-module attributes from a dictionary
simulation_parameters([family])	Return a nested dictionary of module and submodule SimulationParameters
state([family])	Return a nested dictionary of module and submodule States
timed([output_num, dt, add_events])	Convert this module to a TimedModule

__init__(device: Any, config: AfeConfiguration | None = None, dt: float = 0.001, auto_calibrate: bool = False, amplify_level: str = 'low', change_count: int | None = None, hibernation_mode: bool = False, divisive_norm: bool = False, divisive_norm_params: dict | None = {}, calibration_params: dict | None = {}, read_register: bool = False, *args, **kwargs)

Instantiate an AFE module, via a samna backend

Parameters:

• device (AFE2HDK) – A connected AFE2 HDK device.

• config (AFE2Configuration) – A samna AFE2 configuration object.

• dt (float) – The desired spike time resolution in seconds.

• auto_calibrate (bool) – If True, will apply auto-calibration.

• amplify_level (str) – The level of volume gain. Defaul “low” is the one without gain.

• change_count (int) – If is not None, AFE event counter will change from outputting 1 spike out of 4 into outputting 1 out of change_count.

• hibernation_mode (bool) – If True, hibernation mode will be switched on, which only outputs events if it receives inputs above a threshold.

• divisive_norm (bool) – If True, divisive normalization will be switched on.

• divisive_norm_params (Dict) – Specify the divisive normalization parameters, should be structured as {“s”: , “p”: , “iaf_bias”: }.

• calibration_params (Dict) – Specify the calibration parameters.

• read_register (bool) – If True, will print all register values of AFE after initialization.

_abc_impl = <_abc._abc_data object>

_auto_batch(data: ndarray, states: Tuple = (), target_shapes: Tuple | None = None) → Tuple[ndarray, Tuple[ndarray]]

Automatically replicate states over batches and verify input dimensions

Examples

>>> data, (state0, state1, state2) = self._auto_batch(data, (self.state0, self.state1, self.state2))

This will verify that data has the correct final dimension (i.e. self.size_in).

If data has only two dimensions (T, Nin), then it will be augmented to (1, T, Nin). The individual states will be replicated out from shape (a, b, c, ...) to (n_batches, a, b, c, ...) and returned.

If data has only a single dimension (T,), it will be expanded to (1, T, self.size_in).

state0, state1, state2 will be replicated out along the batch dimension.

>>> data, (state0,) = self._auto_batch(data, (self.state0,), ((10, -1, self.size_in),))

Attempt to replicate state0 to a specified size (10, -1, self.size_in).

Parameters:

• data (np.ndarray) – Input data tensor. Either (batches, T, Nin) or (T, Nin)

• states (Tuple) – Tuple of state variables. Each will be replicated out over batches by prepending a batch dimension

• target_shapes (Tuple) – A tuple of target size tuples, each corresponding to each state argument. The individual states will be replicated out to match the corresponding target sizes. If not provided (the default), then states will be only replicated along batches.

Returns:

(np.ndarray, Tuple[np.ndarray]) data, states

_auto_calibration(device: Any, config: AfeConfiguration, calibration_params: dict, apply_config: bool = True) → None

Perform AFE auto-calibration.

Parameters:

• device (XyloA2HDK) – A connected AFE2 HDK device

• config (AFE2Configuration) – A configuration for AFE

• calibration_params (Dict) – Specify the calibration parameters

• apply_config (bool) – If True, will apply configuration to AFE

_force_set_attributes

(bool) If True, do not sanity-check attributes when setting.

_get_attribute_family(type_name: str, family: Tuple | List | str | None = None) → dict

Search for attributes of this module and submodules that match a given family

This method can be used to conveniently get all weights for a network; or all time constants; or any other family of parameters. Parameter families are defined simply by a string: "weights" for weights; "taus" for time constants, etc. These strings are arbitrary, but if you follow the conventions then future developers will thank you (that includes you in six month’s time).

Parameters:

• type_name (str) – The class of parameters to search for. Must be one of ["Parameter", "SimulationParameter", "State"] or another future subclass of ParameterBase

• family (Union[str, Tuple[str]]) – A string or list or tuple of strings, that define one or more attribute families to search for

Returns:

A nested dictionary of attributes that match the provided type_name and family

Return type:

dict

_get_attribute_registry() → Tuple[Dict, Dict]

Return or initialise the attribute registry for this module

Returns:

registered_attributes, registered_modules

Return type:

(tuple)

_has_registered_attribute(name: str) → bool

Check if the module has a registered attribute

Parameters:

name (str) – The name of the attribute to check

Returns:

True if the attribute name is in the attribute registry, False otherwise.

Return type:

bool

_in_Module_init

(bool) If exists and True, indicates that the module is in the __init__ chain.

_name: str | None

Name of this module, if assigned

_register_attribute(name: str, val: ParameterBase)

Record an attribute in the attribute registry

Parameters:

• name (str) – The name of the attribute to register

• val (ParameterBase) – The ParameterBase subclass object to register. e.g. Parameter, SimulationParameter or State.

_register_module(name: str, mod: ModuleBase)

Register a sub-module in the module registry

Parameters:

• name (str) – The name of the module to register

• mod (ModuleBase) – The ModuleBase object to register

_reset_attribute(name: str) → ModuleBase

Reset an attribute to its initialisation value

Parameters:

name (str) – The name of the attribute to reset

Returns:

For compatibility with the functional API

Return type:

self (Module)

_shape

The shape of this module

_spiking_input: bool

Whether this module receives spiking input

_spiking_output: bool

Whether this module produces spiking output

_submodulenames: List[str]

Registry of sub-module names

property _version: Tuple[int, int]

Return the version and revision numbers of the connected Xylo-AFE2 chip

Returns:

version, revision

Return type:

(int, int)

_wrap_recorded_state(recorded_dict: dict, t_start: float) → Dict[str, TimeSeries]

Convert a recorded dictionary to a TimeSeries representation

This method is optional, and is provided to make the timed() conversion to a TimedModule work better. You should override this method in your custom Module, to wrap each element of your recorded state dictionary as a TimeSeries

Parameters:

• state_dict (dict) – A recorded state dictionary as returned by evolve()

• t_start (float) – The initial time of the recorded state, to use as the starting point of the time series

Returns:

The mapped recorded state dictionary, wrapped as TimeSeries objects

Return type:

Dict[str, TimeSeries]

as_graph() → GraphModuleBase

Convert this module to a computational graph

Returns:

The computational graph corresponding to this module

Return type:

GraphModuleBase

Raises:

NotImplementedError – If as_graph() is not implemented for this subclass

attributes_named(name: Tuple[str] | List[str] | str) → dict

Search for attributes of this or submodules by time

Parameters:

name (Union[str, Tuple[str]) – The name of the attribute to search for

Returns:

A nested dictionary of attributes that match name

Return type:

dict

property class_name: str

Class name of self

Type:

str

evolve(input_data, record: bool = False) → Tuple[Any, Any, Any]

Use the AFE HW module to record live audio and return as encoded events

Parameters:

input_data (np.ndarray) – An array [0, T, 0], specifying the number of time-steps to record.

Returns:

(np.ndarray, dict, dict) output_events, {}, {}

property full_name: str

The full name of this module (class plus module name)

Type:

str

modules() → Dict

Return a dictionary of all sub-modules of this module

Returns:

A dictionary containing all sub-modules. Each item will be named with the sub-module name.

Return type:

dict

property name: str

The name of this module, or an empty string if None

Type:

str

parameters(family: Tuple | List | str | None = None) → Dict

Return a nested dictionary of module and submodule Parameters

Use this method to inspect the Parameters from this and all submodules. The optional argument family allows you to search for Parameters in a particular family — for example "weights" for all weights of this module and nested submodules.

Although the family argument is an arbitrary string, reasonable choises are "weights", "taus" for time constants, "biases" for biases…

Examples

Obtain a dictionary of all Parameters for this module (including submodules):

>>> mod.parameters()
dict{ ... }

Obtain a dictionary of Parameters from a particular family:

>>> mod.parameters("weights")
dict{ ... }

Parameters:

family (str) – The family of Parameters to search for. Default: None; return all parameters.

Returns:

A nested dictionary of Parameters of this module and all submodules

Return type:

dict

reset_parameters()

Reset all parameters in this module

Returns:

The updated module is returned for compatibility with the functional API

Return type:

Module

reset_state() → ModuleBase

Reset the state of this module

Returns:

The updated module is returned for compatibility with the functional API

Return type:

Module

save_config(filename)

Save an AFE configuration to disk in JSON format

Parameters:

filename (str) – The filename to write to

set_attributes(new_attributes: dict) → ModuleBase

Set the attributes and sub-module attributes from a dictionary

This method can be used with the dictionary returned from module evolution to set the new state of the module. It can also be used to set multiple parameters of a module and submodules.

Examples

Use the functional API to evolve, obtain new states, and set those states:

>>> _, new_state, _ = mod(input)
>>> mod = mod.set_attributes(new_state)

Obtain a parameter dictionary, modify it, then set the parameters back:

>>> params = mod.parameters()
>>> params['w_input'] *= 0.
>>> mod.set_attributes(params)

Parameters:

new_attributes (dict) – A nested dictionary containing parameters of this module and sub-modules.

property shape: tuple

The shape of this module

Type:

tuple

simulation_parameters(family: Tuple | List | str | None = None) → Dict

Return a nested dictionary of module and submodule SimulationParameters

Use this method to inspect the SimulationParameters from this and all submodules. The optional argument family allows you to search for SimulationParameters in a particular family.

Examples

Obtain a dictionary of all SimulationParameters for this module (including submodules):

>>> mod.simulation_parameters()
dict{ ... }

Parameters:

family (str) – The family of SimulationParameters to search for. Default: None; return all SimulationParameter attributes.

Returns:

A nested dictionary of SimulationParameters of this module and all submodules

Return type:

dict

property size: int

(DEPRECATED) The output size of this module

Type:

int

property size_in: int

The input size of this module

Type:

int

property size_out: int

The output size of this module

Type:

int

property spiking_input: bool

If True, this module receives spiking input. If False, this module expects continuous input.

Type:

bool

property spiking_output

If True, this module sends spiking output. If False, this module sends continuous output.

Type:

bool

state(family: Tuple | List | str | None = None) → Dict

Return a nested dictionary of module and submodule States

Use this method to inspect the States from this and all submodules. The optional argument family allows you to search for States in a particular family.

Examples

Obtain a dictionary of all States for this module (including submodules):

>>> mod.state()
dict{ ... }

Parameters:

family (str) – The family of States to search for. Default: None; return all State attributes.

Returns:

A nested dictionary of States of this module and all submodules

Return type:

dict

timed(output_num: int = 0, dt: float | None = None, add_events: bool = False)

Convert this module to a TimedModule

Parameters:

• output_num (int) – Specify which output of the module to take, if the module returns multiple output series. Default: 0, take the first (or only) output.

• dt (float) – Used to provide a time-step for this module, if the module does not already have one. If self already defines a time-step, then self.dt will be used. Default: None

• add_events (bool) – Iff True, the TimedModule will add events occurring on a single timestep on input and output. Default: False, don’t add time steps.

Returns: TimedModule: A timed module that wraps this module