dsphdl.CICInterpolator

Interpolate signal using CIC filter

Since R2022a

Description

The dsphdl.CICInterpolator System object™ interpolates an input signal by using a cascaded integrator-comb (CIC) interpolation filter. CIC interpolation filters are a class of linear phase finite impulse response (FIR) filters consisting of a comb part and an integrator part. The CIC interpolation filter structure consists of N sections of cascaded comb filters, a rate change factor of R, and N sections of cascaded integrators. For more information about CIC interpolation filters, see Algorithms.

The System object supports these combinations of input and output data.

• Scalar input and scalar output — Support for fixed and variable interpolation rates

• Scalar input and vector output — Support for fixed interpolation rates only

• Vector input and vector output — Support for fixed interpolation rates only

The System object provides an architecture suitable for HDL code generation and hardware deployment.

The System object supports real and complex fixed-point inputs.

To filter input data with a CIC interpolator filter, follow these steps:

1. Create the dsphdl.CICInterpolator object and set its properties.

2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Note

You can also generate HDL code for this hardware-optimized algorithm, without creating a MATLAB^® script, by using the DSP HDL IP Designer app. The app provides the same interface and configuration options as the System object.

Creation

Syntax

cicIntFilt = dsphdl.CICInterpolator

cicIntFilt = dsphdl.CICInterpolator(Name=Value)

Description

cicIntFilt = dsphdl.CICInterpolator creates a CIC interpolator filter System object, cicIntFilt, with default properties.

cicIntFilt = dsphdl.CICInterpolator(Name=Value) creates the filter and sets properties using one or more name-value arguments.

Properties

Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

InterpolationSource — Source of interpolation factor

'Property' (default) | 'Input port'

Specify whether the System object operates with a fixed or variable interpolation rate.

• 'Property' — Use a fixed interpolation rate specified by the InterpolationFactor property.

• 'Input port' — Use a variable interpolation rate specified by the R input argument.

Note

The System object does not support variable interpolation for these two combinations of input and output:

• Scalar input and vector output

• Vector input and vector output

InterpolationFactor — Interpolation factor

2 (default) | integer from 1 to 2048

Specify the interpolation factor as an integer from 1 to 2048. The range of available values depends on the type of input and output data. This value gives the rate at which the System object interpolates the input.

Input Data	Output Data	InterpolationFactor Valid Values
Scalar	Scalar	Integer from 1 to 2048
Scalar	Vector	Integer from 1 to 64
Vector	Vector	Integer from 1 to 64

Note

For vector inputs, select the interpolation factor rate and input vector length such that their multiplication value does not exceed 64.

Dependencies

To enable this property, set the InterpolationSource property to 'Property'.

MaxInterpolationFactor — Upper bound of variable interpolation factor

2 (default) | integer from 1 to 2048

Specify the upper bound of the range of valid values for the R input argument as an integer from 1 to 2048.

Note

The System object does not support variable interpolation for these two combinations of input and output:

• Scalar input and vector output

• Vector input and vector output

Dependencies

To enable this property, set the InterpolationSource property to 'Input port'.

DifferentialDelay — Differential delay

1 (default) | 2

Specify the differential delay of the comb part of the filter as either 1 or 2 cycles.

NumSections — Number of integrator and comb sections

2 (default) | 1 | 3 | 4 | 5 | 6

Specify the number of sections in either the comb part or the integrator part of the System object.

NumCycles — Minimum number of cycles between valid input samples

1 (default) | factors or multiples of R

Specify the minimum number of cycles between the valid input samples as 1, factors of R, or multiples of R based on the type of input and output data, where R is the interpolation factor.

Input Data	Output Data	Minimum Number of Cycles Between Valid Input Samples
Scalar	Scalar	greater than or equal to R
Scalar	Vector	factors less than R
Vector	Vector	1

Dependencies

To enable this property, set the InterpolationSource property to 'Property'.

GainCorrection — Output gain compensation

false (default) | true

Set this property to true to compensate for the output gain of the filter.

The latency of the System object changes depending on the type of interpolation you specify, the number of sections, and the value of this property. For more information on the latency of the System object, see Latency.

OutputDataType — Data type of output

'Full precision' (default) | 'Same word length as input' | 'Minimum section word lengths'

Choose the data type of the filtered output data.

• 'Full precision' — The output data type has a word length equal to the input word length plus gain bits.

• 'Same word length as input' — The output data type has a word length equal to the input word length.

• 'Minimum section word lengths' — The output data type uses the word length you specify in the OutputWordLength property.

OutputWordLength — Word length of output

16 (default) | integer from 2 to 104

Word length of the output, specified as an integer from 2 to 104.

Dependencies

To enable this property, set the OutputDataType property to 'Minimum section word lengths'.

ResetInputPort — Reset argument

false (default) | true

When you set this property to true, the System object expects a reset input argument.

Usage

Syntax

[dataOut,validOut] = cicIntFilt(dataIn,validIn)

[dataOut,validOut] = cicIntFilt(dataIn,validIn,R)

[dataOut,validOut] = cicIntFilt(dataIn,validIn,reset)

[dataOut,validOut] = cicIntFilt(dataIn,validIn,R,reset)

Description

[dataOut,validOut] = cicIntFilt(dataIn,validIn) filters and interpolates the input data using a fixed interpolation factor only when validIn is true.

[dataOut,validOut] = cicIntFilt(dataIn,validIn,R) filters the input data using the specified variable interpolation factor R. The InterpolationSource property must be set to 'Input port'.

[dataOut,validOut] = cicIntFilt(dataIn,validIn,reset) filters the input data when reset is false and clears filter internal states when reset is true. The System object expects the reset argument only when you set the ResetInputPort property to true.

[dataOut,validOut] = cicIntFilt(dataIn,validIn,R,reset) filters the input data when reset is false and clears filter internal states when reset is true. The System object expects the reset argument only when you set the ResetInputPort property to true. The InterpolationSource property must be set to 'Input port'.

Input Arguments

dataIn — Input data

scalar | column vector

Specify input data as a scalar or a column vector with a length from 1 to 64. The input data must be a signed integer or signed fixed point with a word length less than or equal to 32.

While processing the input data, the System object implements the rounding method Floor and the overflow action Wrap. For more details, see Rounding Modes and Overflow Handling.

Data Types: int8 | int16 | int32 | fi
Complex Number Support: Yes

validIn — Indicates valid input data

scalar

Control signal that indicates if the input data is valid. When validIn is 1 (true), the object captures the values from the dataIn argument. When validIn is 0 (false), the object ignores the values from the dataIn argument.

Data Types: logical

R — Variable interpolation rate

scalar

Use this argument to dynamically specify the variable interpolation rate during run time.

This value must have the data type fi(0,12,0) and must be an integer in the range from 1 to the MaxInterpolationFactor property value.

Dependencies

To enable this argument, set the InterpolationSource property to 'Input port'.

Data Types: fi(0,12,0)

reset — Clears internal states

logical scalar

Control signal that clears internal states. When reset is 1 (true), the object stops the current calculation and clears internal states. When the reset is 0 (false) and the input valid is 1 (true), the object captures data for processing.

For more reset considerations, see the Reset Signal section on the Hardware Control Signals page.

Dependencies

To enable this argument, set the ResetInputPort property to true.

Data Types: logical

Output Arguments

dataOut — CIC-interpolated output data

scalar | column vector

CIC-interpolated output data, returned as a scalar or a column vector with a length from 1 to 64.

The OutputDataType property sets the data type of this argument.

Data Types: int8 | int16 | int32 | fi
Complex Number Support: Yes

validOut — Indicates valid output data

scalar

Control signal that indicates if the output data is valid. When validOut is 1 (true), the object returns valid data from the dataOut argument. When validOut is 0 (false), values from the dataOut argument are not valid.

Data Types: logical

ready — Indicates object is ready for new input data

logical scalar

Control signal that indicates that the object is ready for new input data sample on the next cycle. When ready is 1 (true), you can specify the data and valid inputs for the next time step. When ready is 0 (false), the object ignores any input data in the next time step.

Data Types: logical

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

Specific to dsphdl.CICInterpolator

getLatency

Latency of CIC interpolation filter

Common to All System Objects

step	Run System object algorithm
release	Release resources and allow changes to System object property values and input characteristics
reset	Reset internal states of System object

Examples

Create CIC Interpolation Filter for HDL Code Generation

This example shows how to use a dsphdl.CICInterpolator System object™ to filter and upsample data. This object supports scalar and vector inputs. In this example, two functions are provided to work with scalar and vector input data separately. You can generate HDL code from these functions.

Generate Frames of Random Input Samples

Set up workspace variables for the object to use. The object supports fixed and variable interpolation rates for scalar inputs and only a fixed interpolation rate for vector inputs. The example runs the HDLCICInterp_maxR8 function when you set the scalar variable to true and runs the HDLCICInterp_vec function when you set the scalar variable to false. For scalar inputs, choose a range of the input varRValue values and set the interpolation factor value R to the maximum expected interpolation factor. For vector inputs, the input data must be a column vector of size 1 to 64 and R must be an integer multiple of the input frame size.

R = 8;             % interpolation factor
M = 1;             % differential delay
N = 3;             % number of sections
scalar = true;     % true for scalar; false for vector
if scalar
    varRValue = [2, 4, 5, 6, 7, 8];
    vecSize = 1;
else
    varRValue = R; %#ok
    fac = (factor(R));
    vecSize = fac(randi(length(fac),1,1));
end

numFrames = length(varRValue);
dataSamples = cell(1,numFrames);
varRtemp = cell(1,numFrames);
framesize = zeros(1,numFrames);
refOutput = [];
WL = 0;            % Word length
FL = 0;            % Fraction length

Generate Reference Output from dsp.CICInterpolator System Object

Generate frames of random input samples and apply the samples to the dsp.CICInterpolator System object. Later in this example, you use the output generated by the System object as reference data for comparison. The System object does not support a variable interpolation rate, so you must create and release the object for each change in interpolation factor value.

totalsamples = 0;
for i = 1:numFrames
    framesize(i) = varRValue(i)*randi([5 20],1,1);
    dataSamples{i} = fi(randn(vecSize,framesize(i)),1,16,8);
    ref_cic = dsp.CICInterpolator(DifferentialDelay=M, ...
                                  NumSections=N, ...
                                  InterpolationFactor=varRValue(i));
    refOutput = [refOutput,ref_cic(dataSamples{i}(:)).']; %#ok
    release(ref_cic);
end

Run Function Containing dsphdl.CICInterpolator System Object

Set the properties of the System object to match the input data parameters and run the function for your input type. These functions operate on a stream of data samples rather than a frame. You can generate HDL code from these functions.

The example uses the HDLCICInterp_maxR8 function for a scalar input.

function [dataOut,validOut] = HDLCICInterp_maxR8(dataIn,validIn,R)
%HDLCICInterp_maxR8
% Performs CIC interpolation with an input interpolation factor up to 8. 
% sampleIn is a scalar fixed-point value.
% validIn is a logical scalar value.

  persistent cic8;
  if isempty(cic8)
    cic8 = dsphdl.CICInterpolator(InterpolationSource='Input port', ...
                                MaxInterpolationFactor=8, ...
                                DifferentialDelay=1, ...
                                NumSections=3);
  end    
  [dataOut,validOut] = cic8(dataIn,validIn,R);
end
% Copyright 2021-2023 The MathWorks, Inc.

The example uses the HDLCICInterp_vec function for a vector input.

function [dataOut,validOut] = HDLCICInterp_vec(dataIn,validIn)
%HDLCICInterp_vec
% Performs CIC interpolation with an input vector. 
% sampleIn is a fixed-point vector.
% validIn is a logical scalar value.

  persistent cicVec;
  if isempty(cicVec)
    cicVec = dsphdl.CICInterpolator(InterpolationSource='Property', ...
                                    InterpolationFactor=8, ...
                                    DifferentialDelay=1, ...
                                    NumSections=3);
  end    
  [dataOut,validOut] = cicVec(dataIn,validIn);
end
% Copyright 2021-2023 The MathWorks, Inc.

To flush the remaining data, run the object by inserting the required number of idle cycles after each frame using the latency variable. For more information, see the GainCorrection property.

Initialize the output to a size large enough to accommodate the output data. The final size is smaller than totalsamples due to interpolation.

if scalar
    latency = 3 + N + 2*R + 9;
    dataOut = zeros(1,totalsamples*R+numFrames*latency);

else
    latency = 3 + (N*(vecSize*R))+ 3*N + 9; %#ok
    dataOut = zeros(vecSize*R,totalsamples+numFrames*latency);
end
validOut = zeros(1,size(dataOut,2));
idx=0;
for ij = 1:numFrames
    if scalar
        dataIn = upsample([0 0 dataSamples{ij}],R);
        validIn = upsample([true false true(1,length(dataSamples{ij}))],R);

        % scalar input with variable interpolation
        for ii = 1:length(validIn)
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_maxR8( ...
                dataIn(ii), ...
                validIn(ii), ...
                fi(varRValue(ij),0,12,0));
        end
        for ii = 1:latency
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_maxR8( ...
                fi(0,1,16,8), ...
                false, ...
                fi(varRValue(ij),0,12,0));
        end

    else
        % vector input with fixed interpolation
        for ii = 1:size(dataSamples{ij},2)  %#ok
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_vec( ...
                dataSamples{ij}(:,ii), ...
                true);
        end
        for ii = 1:latency
            idx = idx+1;
            [dataOut(:,idx),validOut(idx)] = HDLCICInterp_vec( ...
                fi(zeros(vecSize,1),1,16,8), ...
                false);
        end
    end
end

Compare Function Output with Reference Data

Compare the function results against the output from the dsp.CICInterpolator object.

cicOutput = dataOut(:,validOut==1);
refOutput = refOutput(:);

fprintf('\nCIC Interpolator\n');
difference = (abs(cicOutput(:)-refOutput(1:numel(cicOutput)))>0);
fprintf(['\nTotal number of samples differed between Behavioral ' ...
    'and HDL simulation: %d \n'],sum(difference));

CIC Interpolator

Total number of samples differed between Behavioral and HDL simulation: 0 

Explore Latency of CIC Interpolator Object

The latency of the dsphdl.CICInterpolator System object™ varies depending on how many integrator and comb sections your filter has, the input vector size, and whether you enable gain correction. Use the getLatency function to find the latency of a particular filter configuration. The latency is the number of cycles between the first valid input and the first valid output, assuming the input is continuously valid.

Create a dsphdl.CICInterpolator System object and request the latency. The default System object filter has two integrator and comb sections, and the gain correction is disabled.

hdlcic = dsphdl.CICInterpolator

hdlcic = 
  dsphdl.CICInterpolator with properties:

    InterpolationSource: 'Property'
    InterpolationFactor: 2
      DifferentialDelay: 1
            NumSections: 2
              NumCycles: 1
         GainCorrection: false

  Use get to show all properties

L_def = getLatency(hdlcic)

L_def = 
13

Modify the filter object so it has three integrator and comb sections. Check the resulting change in latency.

hdlcic.NumSections = 3;
L_3sec = getLatency(hdlcic)

L_3sec = 
18

Enable the gain correction on the filter object with vector input size 2. Check the resulting change in latency.

hdlcic.GainCorrection = true;
vecSize = 2;
L_wgain = getLatency(hdlcic,vecSize)

L_wgain = 
33

Algorithms

CIC Interpolation Filter

The transfer function of a CIC interpolation filter is

$$\begin{array}{l}H(z)={\left[{\displaystyle \sum _{k=0}^{RM-1}{z}^{-k}}\right]}^N=\frac{{(1-z^{-RM})}^N}{{(1-z^{-1})}^N}=\frac{{(1-z^{-RM})}^N}{1}·\frac{1}{{(1-z^{-1})}^N}=H_{\text{C}}{}^N(z)·H_{\text{I}}{}^N(z).\\ \end{array}$$

• H_C is the transfer function of the comb part of the CIC filter.

• H_I is the transfer function of the integrator part of the CIC filter.

• N is the number of sections in either the comb part or integrator part of the filter. This value does not represent the total number of sections throughout the entire filter.

• R is the interpolation factor.

• M is the differential delay.

CIC Filter Structure

The dsphdl.CICInterpolator System object has the CIC filter structure shown in this figure. The structure consists of N sections of cascaded comb filters, a rate change factor of R, and N sections of cascaded integrators [1].

You can locate the unit delay in the integrator part of the CIC filter in either the feedforward or feedback path. These two configurations yield an identical filter frequency response. However, the numerical outputs from these two configurations are different due to the latency of the paths. Because this configuration is preferred for HDL implementation, this System object puts the unit delay in the feedforward path of the integrator.

Fixed and Variable Interpolation

The System object upsamples the comb stage output using R, either using the fixed interpolation rate provided using the InterpolationFactor property or the variable interpolation rate provided using the R input argument. At the upsampling stage, the System object uses a counter to count the valid input samples, which depend on the interpolation rate. Whenever the interpolation rate changes, the System object resets and starts a new calculation from the next sample. This mechanism prevents the object from accumulating false values. Then, the System object provides the interpolated output to the integrator part of the CIC filter.

Gain Correction

The gain of the CIC interpolation filter at each stage is given by

$$G_i=\left\{\begin{array}{cc}{2}^i& i=1,2,\mathrm{...},N\\ \frac{2^{2N-i}{\left(RM\right)}^{i-N}}{R}& i=N+1,\mathrm{...},2N\end{array}\right\}.$$

• G_i is the gain at ith stage.

• R is the InterpolationFactor property value.

• M is the DifferentialDelay property value.

• N is the NumSections property value.

The output of the System object is amplified by a specific gain value. This gain equals the gain of the 2Nth stage of the CIC interpolation filter and is given by $$Gain=\frac{{(R\text{x}M)}^N}{R}$$.

The System object implements gain correction in two parts: coarse gain and fine gain. In coarse gain correction, the System object calculates the shift value, adds the shift value to the fractional bits to create a numeric type, and performs a bit-shift left and reinterpretcast. In fine gain correction, the System object divides the remaining gain with the coarse gain if the gain is not a power of 2. Then, the System object multiplies the corrected coarse gain value by the inverse value of the fine gain. Before the System object starts processing, all possible shift and fine gain values are precalculated and stored in an array.

You can modify this equation to $$Gain=2^{cGain}\text{x}fGain$$. In this equation, cGain is the coarse gain and fGain is the fine gain. These gains are given by these equations.

• $$cGain=floor({\log }_{2}Gain)$$

• $$fGain=Gain/2^{cGain}=Gain\text{x}{2}^{-cGain}{}^{}$$

To perform gain correction when the InterpolationSource property is set to 'Input port', the System object sets the output data type configured with the maximum interpolation rate and bit-shifts left for all of the values under the maximum interpolation rate. The bit-shift value is equal to $$Maximumgain-{\log }_2(currentgain)$$.

Output Data Type

This section explains how the System object outputs data is based on the output data type selection. Consider a System object with R, M, and N values of 8, 1, and 3, respectively, and an input width of 16. The word length at the ith stage is calculated as $$B_i=B_{\text{In}}+[{\log }_2(G_i)]$$, where:

• G_i is the gain at ith stage.

• B_In is the input word length.

• B_i is the word length at ith stage.

The output word length is calculated as $$B_{Out}=B_{In}+N-1$$, where B_Out is the output word length.

When you set the OutputDataType property to 'Full precision', the System object returns data with a word length of 22 by adding 6 gain bits to the input word length of 16. The word lengths of the internal comb and integrator stages are set to accommodate the bit growth.

When you set the OutputDataType property to 'Same word length as input', the object outputs data with a word length of 16, which is the same length as the input word length. The word lengths of the internal comb and integrator stages are set in the same way as in 'Full precision' mode.

When you set the OutputDataType property to 'Minimum section word lengths' and the OutputWordLength to 16, the System object returns data with a word length of 16. The word lengths of the internal comb and integrator stages are set in the same way as in 'Full precision' mode.

Latency

The latency of the System object changes depending on the type of input, the interpolation you specify, the value of the NumSections property, GainCorrection property, and the NumCycles property. This table shows the latency of the System object. N is the number of sections, vecLen is the length of the vector, and R is the interpolation factor.

Common latency is equal to 2 + (N x (vecLen x R)) + 3 x N, when R is equal to 1 and it is equal to 3 + (N x (vecLen x R)) + 3 x N, when R is greater than 1.

Input Data	Output Data	Interpolation Type	Gain Correction	Minimum number of cycles between valid input samples (NumCycles)	Latency in Clock Cycles
Scalar	Scalar	Fixed	off	NumCycles = R and > R	3 + N. 2 + N, when R = 1.
			on	NumCycles = R and > R	3 + N + 9. 2 + N + 9, when R = 1.
Scalar	Scalar	Variable	off	NA	3 + N + 2 x Rmax. 2 + N, when Rmax = 1.
			on	NA	2 + N + 2 x Rmax + 9. 2 + N + 9, when Rmax = 1.
Scalar	Vector	Fixed	off	NumCycles = 1	Common latency + 1, when R is greater than N. Common latency, when R is less than or equal to N. Common latency – (1 + floor(N/(3 x R))), when R is less than N and (vecLen == 2 && (R == 2 && (N == 4 || N == 5 || N == 6)) || (R== 3 && N == 6)).
				NumCycles < R	3 + N + ((R + 1) x N + 2) + 1 + (N – 1) x NumCycles.
			on	NumCycles = 1	Common latency + 1 + 9, when R is greater than N. Common latency + 9, when R is less than or equal to N. Common latency – (1 + floor(N/(3 x R))) + 9, when R is less than N and (vecLen == 2 && (R == 2 && (N == 4 || N == 5 || N == 6)) || (R == 3 && N == 6)).
				NumCycles < R	3 + N + ((R + 1) x N + 2) + 1 + (N – 1) x NumCycles + 9.
Vector	Vector	Fixed	off	NumCycles = 1	Common latency Common latency – 1, when (vecLen == 2 && (R == 2 && (N == 4 || N == 5 || N == 6)) || (R== 3 && N == 6)) || (vecLen == 3 && (R == 2 && N == 6)). Common latency – ((N >1) + (N > 4)), when R = 1 and vecLen == 2. Common latency – ((N > (vecLen – 1)), when R = 1 and vecLen > 2.
			on	NumCycles = 1	Common latency + 9. Common latency – 1 + 9, when (vecLen == 2 && (R == 2 && (N == 4 || N == 5 || N == 6)) || (R== 3 && N == 6)) || (vecLen == 3 && (R == 2 && N == 6)). Common latency – ((N >1) + (N > 4)) + 9, when R = 1 and vecLen == 2. Common latency – ((N > (vecLen – 1)) + 9, when R = 1 and vecLen > 2.

Note

The System object does not support variable interpolation for these two combinations of input and output:

• Scalar input and vector output

• Vector input and vector output

Scalar Input

This section shows the output of the System object for a scalar input with different R, M, and N values.

This figure shows the output of the System object with the default configuration (that is, with a fixed interpolation rate and R, M, and N values of 2, 1, and 2, respectively). The latency of the System object is 5 clock cycles and is calculated as 3 + N, where N is the number of sections.

This figure shows the output of the System object with a fixed interpolation rate, R, M, and N values of 8, 1, and 3, respectively, and GainCorrection set to true. The latency of the object is 15 clock cycles and is calculated as 3 + N + 9, where N is the number of sections.

This figure shows the output of the System object with variable interpolation rate (R input argument) values of 2, 4, and 8 and with M and N values of 1 and 3, respectively. In this case, the GainCorrection property is set to false. The System object accepts R argument value changes only when validIn is 1 (true). The latency of the System object is 22 clock cycles and is calculated as 3 + N + 2 x R_max, where N is the number of sections and R_max is the maximum interpolation factor.

Vector Input

This section shows the output of the System object for a vector input with different R, M, and N values.

This figure shows the output of the System object for a two-element column vector input with the default configuration, that is, with a fixed interpolation rate and R, M, and N values of 2, 1, and 2, respectively. The latency of the System object is 38 clock cycles.

This figure shows the output of the System object for an eight-element column vector input with a fixed interpolation rate, R, M, and N values of 8, 1, and 3, respectively, and the GainCorrection property to true. The latency of the System object is 209 clock cycles.

Performance

The performance of the synthesized HDL code varies with your target and synthesis options. It also depends on the input data type.

This table shows the resource and performance data synthesis results of the System object for a scalar input with fixed and variable interpolation rates and for a two-element column vector of type fixdt(1,16,0) with a fixed interpolation rate when R, M, and N are 2, 1, and 2, respectively. The generated HDL code is targeted to the AMD^® Zynq^®- 7000 ZC706 Evaluation Board.

Input Data	Interpolation Type	Slice LUTs	Slice Registers	Maximum Frequency in MHz
Scalar	Fixed rate	68	90	844.12
	Variable rate	143	115	451.83
Vector	Fixed rate	480	921	376.51

The resources and frequencies vary based on the type of input data, and the values of R, M, and N, as well as other properties.

References

[1] Hogenauer, E. “An Economical Class of Digital Filters for Decimation and Interpolation.” IEEE Transactions on Acoustics, Speech, and Signal Processing 29, no. 2 (April 1981): 155–62. https://doi.org/10.1109/TASSP.1981.1163535.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

This System object supports C/C++ code generation for accelerating MATLAB simulations, and for DPI component generation.

HDL Code Generation

Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

The software supports double and single data types for simulation, but not for HDL code generation.

To generate HDL code from predefined System objects, see HDL Code Generation from Viterbi Decoder System Object (HDL Coder).

Version History

Introduced in R2022a