## Programmable Filter Coefficients for FIR Filters

**Note**

The Filter Design HDL Coder™ product will be discontinued in a future release. Instead, you can model
hardware behavior, and generate HDL code by using System objects or Simulink^{®} blocks from DSP HDL Toolbox™. These objects and blocks include hardware-friendly control signals and
architecture options. To generate HDL code from DSP HDL Toolbox objects and blocks, you must also have the HDL Coder™ product.

For examples of modeling a programmable FIR filter for hardware, see Programmable FIR Filter for FPGA and Optimize Programmable FIR Filter Resources (DSP HDL Toolbox). These examples use the
Discrete FIR Filter (DSP HDL Toolbox) block. Equivalent functionality is also available in the
`dsphdl.FIRFilter`

(DSP HDL Toolbox)
System object™.

By default, the coder obtains filter coefficients from a filter object and hard-codes them into the generated code. An HDL filter realization generated in this way cannot be used with a different set of coefficients.

For direct form FIR filters, the coder provides UI options and corresponding command-line properties that let you:

Generate an interface for loading coefficients from memory. Coefficients stored in memory are called

*programmable coefficients*.Test the interface.

Programmable filter coefficients are supported for these direct form FIR filter types:

Direct form

Direct form symmetric

Direct form antisymmetric

To use programmable coefficients, a port interface (referred to as a *processor
interface*) is generated for the filter entity or module. Coefficient loading is
assumed to be under the control of a microprocessor that is external to the generated filter.
The filter uses the loaded coefficients for processing input samples.

Programmable filter coefficients are supported for these filter architectures:

`Fully parallel`

`Fully serial`

`Partly serial`

`Cascade serial`

When you choose a serial FIR filter architecture, you can also specify how the coefficients are stored. You can select a dual-port or single-port RAM, or a register file. See Programmable Filter Coefficients for FIR Filters.

You can also generate a processor interface for loading IIR filter coefficients. See Programmable Filter Coefficients for IIR Filters.

### UI Options for Programmable Coefficients

These UI options let you specify programmable coefficients.

The

**Coefficient source**list on the Generate HDL tool lets you select whether coefficients are obtained from the filter object and hard-coded (`Internal`

), or from memory (`Processor interface`

). The default is`Internal`

.The corresponding command-line property is

`CoefficientSource`

.The

**Coefficient stimulus**option on the**Test Bench**pane of the Generate HDL tool specifies how the test bench tests the generated memory interface.The corresponding command-line property is

`TestBenchCoeffStimulus`

.

### Generating a Test Bench for Programmable FIR Coefficients

This section describes how to use the `TestBenchCoeffStimulus`

property to specify how the test bench drives the
coefficient ports. You can also use the **Coefficient stimulus** option for
this purpose.

When a coefficient memory interface has been generated for a filter, the coefficient
ports have associated test vectors. The `TestbenchCoeffStimulus`

property
determines how the test bench drives the coefficient ports.

The `TestBenchStimulus`

property determines the filter input stimuli.

The `TestbenchCoeffStimulus`

property selects from two types of test
benches. `TestbenchCoeffStimulus`

takes a vector argument. The valid values
are:

`[]`

: Empty vector. (default)When the value of

`TestbenchCoeffStimulus`

is an empty vector, the test bench loads the coefficients from the filter object, and then forces the input stimuli. This test verifies that the interface writes one set of coefficients into the memory without encountering an error.[

`coeff1`

,`coeff2`

,...`coeffN`

]: Vector of`N`

coefficients, where`N`

is determined as follows:For symmetric filters,

`N`

must equal`ceil(length(filterObj.Numerator)/2)`

.For antisymmetric filters,

`N`

must equal`floor(length(filterObj.Numerator)/2)`

.For other filters,

`N`

must equal the length of the filter object.

In this case, the filter processes the input stimuli twice. First, the test bench loads the coefficients from the filter object and forces the input stimuli to show the response. Then, the filter loads the set of coefficients specified in the

`TestbenchCoeffStimulus`

vector, and shows the response by processing the same input stimuli for a second time. In this case, the internal states of the filter, as set by the first run of the input stimulus, are retained. The test bench verifies that the interface writes two different sets of coefficients into the coefficient memory. The test bench also provides an example of how the memory interface can be used to program the filter with different sets of coefficients.

**Note**

If a coefficient memory interface has not been previously generated for the filter,
the `TestbenchCoeffStimulus`

property is ignored.

For an example, see Test Bench for FIR Filter with Programmable Coefficients.

### Using Programmable Coefficients with Serial FIR Filter Architectures

This section discusses special considerations for using programmable filter coefficients with FIR filters that have one of these serial architectures.

`Fully serial`

`Partly serial`

`Cascade serial`

#### Specifying Memory for Programmable Coefficients

By default, the processor interface for programmable coefficients loads the
coefficients from a register file. The **Coefficient memory** pull-down
menu lets you specify alternative RAM-based storage for programmable coefficients.

You can set **Coefficient memory** when:

The filter is a FIR filter.

You set

**Coefficient source**to`Processor interface`

.You set

**Architecture**to`Fully serial`

,`Partly serial`

, or`Cascade serial`

.

The figure shows the **Coefficient memory** option for a fully serial
FIR filter. You can select an option using the drop-down list.

The table summarizes the **Coefficient memory** options.

Coefficient memory Selection | Description |
---|---|

`Registers` | default: Store programmable coefficients in a register
file. |

`Single Port RAMs` | Store programmable coefficients in single-port RAM. The coder writes each RAM and its interface to a separate file. The number of generated RAMs depends on the filter partitioning. |

`Dual Port RAMs` | Store programmable coefficients in dual-port RAM. The coder writes each RAM and its interface to a separate file. The number of generated RAMs depends on the filter partitioning. |

#### Timing Considerations

In a serial implementation of a FIR filter, the rate of the system clock
(`clk`

) is generally a multiple of the input data rate (the sample rate
of the filter). The exact relationship between the clock rate and the data rate depends on
your choice of serial architecture and partitioning options.

Programmable coefficients load into the `coeffs_in`

port at either
the system clock rate (faster) or the input data (slower) rate. If your design requires
loading of coefficients at the faster rate, observe these points.

When

`write_enable`

asserts, coefficients load from the`coeffs_in`

port into coefficient memory at the address specified by`write_address`

.`write_done`

can assert for anynumber of clock cycles. If`write_done`

asserts at least two`clk`

cycles before the arrival of the next data input value, new coefficients will be applied with the next data sample. Otherwise, new coefficients will be applied for the data after the next sample.

These two examples illustrate how serial partitioning affects the timing of coefficient loading.

Create a direct form filter with 11 coefficients.

rng(13893,'v5uniform'); b = rand(1,11); filt = dsp.FIRFilter('Numerator',b,'Structure','Direct form');

Generate VHDL code for `filt`

, using a partly serial architecture
with the serial partition `[7 4]`

. Set
`CoefficientSource`

to generate a processor interface.

generatehdl(filt,'InputDataType',numerictype(1,16,15), ... 'SerialPartition',[7 4],'CoefficientSource','ProcessorInterface');

### Clock rate is 7 times the input sample rate for this architecture. ### HDL latency is 3 samples

This partitioning results in a clock rate that is seven times the input sample rate.

The timing diagram illustrates the rate of coefficient loading relative to the rate of
input data samples. While `write_enable`

is asserted, 11 coefficient
values are loaded, via `coeffs_in`

, to 11 sequential memory addresses. On
the next `clk`

cycle, `write_enable`

is deasserted and
`write_done`

is asserted for one clock period. The coefficient loading
operation is completed within two cycles of data input, allowing 2 `clk`

cycles to elapse before the arrival of the data value `07FFF`

. Therefore
the newly loaded coefficients are applied to that data sample.

Now define a serial partition of `[6 5]`

for the same filter. This
partition results in a slower clock rate, six times the input sample rate.

generatehdl(filt,'InputDataType',numerictype(1,16,15), ... 'SerialPartition',[6 5],'CoefficientSource','ProcessorInterface');

### Clock rate is 6 times the input sample rate for this architecture. ### HDL latency is 3 samples

The timing diagram illustrates that `write_done`

deasserts too late
for the coefficients to be applied to the arriving data value `278E`

.
They are applied instead to the next sample, `7FFF`

.