Second-order IIR Filter with ArchitectureΒΆ

from b_asic.architecture import Architecture, Memory, ProcessingElement
from b_asic.core_operations import Addition, ConstantMultiplication
from b_asic.schedule import Schedule
from b_asic.scheduler import ASAPScheduler
from b_asic.sfg import SFG
from b_asic.special_operations import Delay, Input, Output

in1 = Input("IN1")
c0 = ConstantMultiplication(5, in1, "C0")
add1 = Addition(c0, None, "ADD1")
T1 = Delay(add1, 0, "T1")
T2 = Delay(T1, 0, "T2")
b2 = ConstantMultiplication(0.2, T2, "B2")
b1 = ConstantMultiplication(0.3, T1, "B1")
add2 = Addition(b1, b2, "ADD2")
add1.input(1).connect(add2)
a1 = ConstantMultiplication(0.4, T1, "A1")
a2 = ConstantMultiplication(0.6, T2, "A2")
add3 = Addition(a1, a2, "ADD3")
a0 = ConstantMultiplication(0.7, add1, "A0")
add4 = Addition(a0, add3, "ADD4")
out1 = Output(add4, "OUT1")

sfg = SFG(inputs=[in1], outputs=[out1], name="Second-order direct form IIR filter")

The SFG is

sfg
%3 in0 IN1 (in0) cmul0 C0 (cmul0) in0:e->cmul0 add0 ADD1 (add0) cmul0->add0 0 out0 OUT1 (out0) add2 ADD4 (add2) add2->out0:w add0.0 add0->add0.0 add1 ADD2 (add1) add1->add0 1 t0 T1 (t0) add0.0->t0 cmul1 A0 (cmul1) add0.0->cmul1 t0.0 t0->t0.0 cmul1->add2 0 add3 ADD3 (add3) add3->add2 1 cmul2 A1 (cmul2) cmul2->add3 0 cmul3 A2 (cmul3) cmul3->add3 1 t1.0 t1.0->cmul3 cmul4 B2 (cmul4) t1.0->cmul4 t1 T2 (t1) t1->t1.0 t0.0->cmul2 t0.0->t1 cmul5 B1 (cmul5) t0.0->cmul5 cmul4->add1 1 cmul5->add1 0


Set latencies and execution times.

sfg.set_latency_of_type(ConstantMultiplication, 2)
sfg.set_latency_of_type(Addition, 1)
sfg.set_execution_time_of_type(ConstantMultiplication, 1)
sfg.set_execution_time_of_type(Addition, 1)

Create schedule.

schedule = Schedule(sfg, scheduler=ASAPScheduler(), cyclic=True)
schedule
2026-06-03T14:26:30.318579 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/


Reschedule to only require one adder and one multiplier.

schedule.move_operation('add3', 2)
schedule.move_operation('cmul4', -4)
schedule.move_operation('cmul3', -5)
schedule.move_operation('cmul5', -2)
schedule.move_operation('cmul2', 1)
schedule
2026-06-03T14:26:30.448682 image/svg+xml Matplotlib v3.10.9, https://matplotlib.org/


Extract operations and create processing elements.

operations = schedule.get_operations()
adders = operations.get_by_type_name('add')
adders.show(title="Adder executions")
mults = operations.get_by_type_name('cmul')
mults.show(title="Multiplier executions")
inputs = operations.get_by_type_name('in')
inputs.show(title="Input executions")
outputs = operations.get_by_type_name('out')
outputs.show(title="Output executions")

adder = ProcessingElement(adders, entity_name="adder")
multiplier = ProcessingElement(mults, entity_name="multiplier")
pe_in = ProcessingElement(inputs, entity_name='input')
pe_out = ProcessingElement(outputs, entity_name='output')
  • Adder executions
  • Multiplier executions
  • Input executions
  • Output executions

Extract and assign memory variables.

mem_vars = schedule.get_memory_variables()
mem_vars.show(title="All memory variables")
direct, mem_vars = mem_vars.split_on_length()
mem_vars.show(title="Non-zero time memory variables")
mem_vars_set = mem_vars.split_on_ports(read_ports=1, write_ports=1, total_ports=2)

memories = []
for i, mem in enumerate(mem_vars_set):
    memory = Memory(mem, memory_type="RAM", entity_name=f"memory{i}")
    memories.append(memory)
    mem.show(title=f"{memory.entity_name}")
    memory.assign("left_edge")
    memory.show_content(title=f"Assigned {memory.entity_name}")

direct.show(title="Direct interconnects")
  • All memory variables
  • Non-zero time memory variables
  • memory0
  • Assigned memory0
  • memory1
  • Assigned memory1
  • memory2
  • Assigned memory2
  • Direct interconnects

Create architecture.

arch = Architecture(
    {adder, multiplier, pe_in, pe_out}, memories, direct_interconnects=direct
)

The architecture can be rendered in enriched shells.

arch
%3 cluster_memories Memories cluster_pes Processing Elements cluster_io_in Inputs cluster_io_out Outputs memory0 0 memory0 : (RAM, 1 cell) 0 _wl_out_12 memory0 memory0:e->_wl_out_12 memory1 0 memory1 : (RAM, 2 cells) 0 _wl_out_15 memory1 memory1:e->_wl_out_15 memory2 0 memory2 : (RAM, 1 cell) 0 _wl_out_6 memory2 memory2:e->_wl_out_6 multiplier 0 multiplier 0 _wl_out_8 multiplier multiplier:e->_wl_out_8 adder 0 adder 0 1 _wl_out_0 adder adder:e->_wl_out_0 input input 0 multiplier_in0_mux 0 multiplier_in0_mux 0 1 2 input:e->multiplier_in0_mux:w output 0 output multiplier_in0_mux:e->multiplier:w adder_in0_mux 0 adder_in0_mux 0 1 adder_in0_mux:e->adder:w memory0_in0_mux 0 memory0_in0_mux 0 1 memory0_in0_mux:e->memory0:w adder_in1_mux 0 adder_in1_mux 0 1 2 adder_in1_mux:e->adder:w _wl_in_1 adder _wl_in_1:e->adder_in1_mux:w _wl_in_2 adder _wl_in_2:e->memory0_in0_mux:w _wl_in_3 adder _wl_in_3:e->output:w _wl_in_4 adder _wl_in_4:e->multiplier_in0_mux:w _wl_in_5 adder _wl_in_5:e->memory2:w _wl_in_7 memory2 _wl_in_7:e->multiplier_in0_mux:w _wl_in_9 multiplier _wl_in_9:e->memory1:w _wl_in_10 multiplier _wl_in_10:e->adder_in0_mux:w _wl_in_11 multiplier _wl_in_11:e->memory0_in0_mux:w _wl_in_13 memory0 _wl_in_13:e->adder_in1_mux:w _wl_in_14 memory0 _wl_in_14:e->adder_in0_mux:w _wl_in_16 memory1 _wl_in_16:e->adder_in1_mux:w


Total running time of the script: (0 minutes 1.498 seconds)

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