Various cleanups, performance improvements, and new method

b_asic/architecture.py

@@ -265,7 +265,7 @@ class Resource(HardwareBlock):
         self.plot_content(ax, **kwargs)
         height = 0.4
         if title:
-            height += 0.4
+            height = 0.8
             fig.suptitle(title)
         fig.set_figheight(math.floor(max(ax.get_ylim())) * 0.3 + height)
         fig.show()  # type: ignore

b_asic/signal_flow_graph.py

@@ -7,7 +7,7 @@ Contains the signal flow graph operation.
 import itertools
 import re
 import warnings
-from collections import defaultdict, deque
+from collections import Counter, defaultdict, deque
 from collections.abc import Iterable, MutableSet, Sequence
 from fractions import Fraction
 from io import StringIO
@@ -2066,6 +2066,10 @@ class SFG(AbstractOperation):
                     paths.append(newpath)
         return paths
 
+    def operation_counter(self) -> Counter:
+        """Return a Counter with the number of instances for each type."""
+        return Counter(op.type_name() for op in self.operations)
+
     def edit(self) -> dict[str, "SFG"]:
         """Edit SFG in GUI."""
         from b_asic.GUI.main_window import start_editor

examples/auto_scheduling_with_custom_io_times.py

 """
 =========================================
-Auto Scheduling With Custom IO times
+Automatic scheduling with custom IO times
 =========================================
 
+It is possible to specify the IO times and provide those to the scheduling.
 """
 
 from b_asic.core_operations import Butterfly, ConstantMultiplication
@@ -31,7 +32,8 @@ schedule1 = Schedule(sfg, scheduler=ASAPScheduler())
 schedule1.show()
 
 # %%
-# Generate a non-cyclic Schedule from HybridScheduler with custom IO times.
+# Generate a non-cyclic Schedule from HybridScheduler with custom IO times,
+# one input and output per time unit
 resources = {Butterfly.type_name(): 1, ConstantMultiplication.type_name(): 1}
 input_times = {f"in{i}": i for i in range(points)}
 output_delta_times = {f"out{i}": i for i in range(points)}

examples/latency_offset_scheduling.py

 """
-================================
-Automatic Scheduling for different latency-offsets.
-================================
+==================================================
+Automatic scheduling for different latency-offsets
+==================================================
 
 This example showcases how one can generate a schedule where the
 operations have different latency offsets for the different inputs/outputs.

examples/ldlt_matrix_inverse.py

 """
-=========================================
-LDLT Matrix Inversion Algorithm
-=========================================
+===============================
+LDLT matrix inversion algorithm
+===============================
 
+This provides some examples of the different list-based schedulers that are
+available in B-ASIC.
 """
 
 from b_asic.architecture import Memory, ProcessingElement
@@ -44,26 +46,27 @@ print("Scheduling time:", schedule.schedule_time)
 schedule.show()
 
 # %%
-# Create an EarliestDeadline schedule that satisfies the resource constraints.
+# Create an earliest deadline schedule that uses one MADS and one Reciprocal PE.
 resources = {MADS.type_name(): 1, Reciprocal.type_name(): 1}
 schedule = Schedule(sfg, scheduler=EarliestDeadlineScheduler(resources))
 print("Scheduling time:", schedule.schedule_time)
 schedule.show()
 
 # %%
-# Create a LeastSlackTime schedule that satisfies the resource constraints.
+# Create a least slack-time schedule that uses one MADS and one Reciprocal PE.
 schedule = Schedule(sfg, scheduler=LeastSlackTimeScheduler(resources))
 print("Scheduling time:", schedule.schedule_time)
 schedule.show()
 
 # %%
-# Create a MaxFanOutScheduler schedule that satisfies the resource constraints.
+# Create a max fan-out schedule that uses one MADS and one Reciprocal PE.
 schedule = Schedule(sfg, scheduler=MaxFanOutScheduler(resources))
 print("Scheduling time:", schedule.schedule_time)
 schedule.show()
 
 # %%
-# Create a HybridScheduler schedule that satisfies the resource constraints with custom IO times.
+# Create a HybridScheduler schedule that one MADS and one Reciprocal PE with
+# custom IO times.
 # This is the schedule we will synthesize an architecture for.
 input_times = {
     "in0": 0,