Spiker with 32bit

Author: phstratmann

src/lava/proc/spiker/process.py

@@ -3,6 +3,7 @@
 # See: https://spdx.org/licenses/
 
 import typing as ty
+import numpy.typing as npty
 
 import numpy as np
 from lava.magma.core.process.ports.ports import OutPort
@@ -19,6 +20,7 @@ class Spiker(AbstractProcess):
         Shape of the population of process units.
     period : int
         Number of timesteps between subsequent emissions of payload.
+        Note that the first spike is emitted at time step period + 1.
     payload : int
         A value to be send with every output message.
     name : str
@@ -39,3 +41,74 @@ def __init__(self, *,
         self.rate = Var(shape=shape, init=period)
         self.counter = Var(shape=shape, init=np.zeros(shape).astype(int))
         self.payload = Var(shape=shape, init=payload)
+
+
+class Spiker32bit(AbstractProcess):
+    """Process emitting a specified payload at a given rate.
+    Provides 32bit payloads, and separate payloads for each neuron.
+    Other than the default Spiker process, this process actually starts spiking
+    at timestep = period.
+
+    Parameters
+    ----------
+    shape : tuple(int)
+        Shape of the population of process units.
+    period : int
+        Number of timesteps between subsequent emissions of payload.
+    payload : int
+        A value to be send with every output message.
+        Can be in [0, 2**32 - 1] if signed==False,
+        or in [-2**31, 2**31 - 1] if signed==True.
+    signed : bool
+        True if signed payload, False otherwise.
+    name : str
+        Name of the Process. Default is 'Process_ID', where ID is an
+        integer value that is determined automatically.
+    log_config : LogConfig
+        Configuration options for logging.
+    """
+
+    def __init__(self, *,
+                 shape: ty.Tuple[int, ...] = (1,),
+                 period: ty.Union[int, npty.ArrayLike] = 1,
+                 payload: ty.Union[int, npty.ArrayLike] = 1,
+                 name: ty.Optional[str] = None,
+                 log_config: ty.Optional[LogConfig] = None) -> None:
+
+        signed = self._input_validation(payload)
+
+        if np.isscalar(period):
+            period = np.array([period], dtype=int)
+        else:
+            period = period.astype(int)
+        if np.isscalar(payload):
+            payload = np.array([payload])
+        else:
+            payload = payload.astype(int)
+
+        super().__init__(shape=shape,
+                         period=period,
+                         payload=payload,
+                         signed=signed,
+                         name=name, log_config=log_config)
+        self.s_out = OutPort(shape=shape)
+        self.counter = Var(shape=shape, init=np.zeros(shape).astype(int) + 1)
+
+    def _input_validation(self, payload) -> bool:
+        payload_min = np.min(payload)
+        payload_max = np.max(payload)
+        signed = payload_min < 0
+
+        if payload_min < -2 ** 31:
+            raise ValueError(
+                f"The payload must be >= -2**31, but the smallest value is "
+                f"{payload_min}.")
+
+        payload_max_allowed = 2 ** 31 - 1 if signed else 2 ** 32 - 1
+
+        if payload_max > payload_max_allowed:
+            raise ValueError(
+                f"The payload must be <= {payload_max_allowed}, but the "
+                f"largest value is  {payload_max}.")
+
+        return signed