To completely understand the results generated by fixed-point Simulink^{®} blocks,
you must be aware of these issues:

When numerical block parameters are converted from doubles to Fixed-Point Designer™ data types

When input signals are converted from one Fixed-Point Designer data type to another (if at all)

When arithmetic operations on input signals and parameters are performed

For example, suppose a fixed-point Simulink block performs an arithmetic operation on its input signal and a parameter, and then generates output having characteristics that are specified by the block. The following diagram illustrates how these issues are related.

The sections that follow describe parameter and signal conversions. discusses arithmetic operations.

Parameters of fixed-point blocks that accept numerical values
are always converted from `double`

to a fixed-point
data type. Parameters can be converted to the input data type, the
output data type, or to a data type explicitly specified by the block.
For example, the Discrete FIR Filter block
converts its **Initial states** parameter to the
input data type, and converts its **Numerator coefficient** parameter
to a data type you explicitly specify via the block dialog box.

Parameters are always converted before any arithmetic operations
are performed. Additionally, parameters are always converted *offline* using
round-to-nearest and saturation. Offline conversions are discussed
below.

An offline conversion is a conversion performed by your development platform (for example, the processor on your PC), and not by the fixed-point processor you are targeting. For example, suppose you are using a PC to develop a program to run on a fixed-point processor, and you need the fixed-point processor to compute

$$y=\left(\frac{ab}{c}\right)u=Cu$$

over and over again. If *a*, *b*,
and *c* are constant parameters, it is inefficient
for the fixed-point processor to compute *ab*/*c* every
time. Instead, the PC's processor should compute *ab*/*c* offline
one time, and the fixed-point processor computes only *C*·*u*.
This eliminates two costly fixed-point arithmetic operations.

Consider the conversion of a real-world value from one fixed-point data type to another. Ideally, the values before and after the conversion are equal.

$${V}_{a}={V}_{b},$$

where *V _{b}* is
the input value and

$${V}_{i}={F}_{i}{2}^{{E}_{i}}{Q}_{i}+{B}_{i}.$$

Solving for the output data type's stored integer value, *Q*_{a} is
obtained:

$$\begin{array}{c}{Q}_{a}=\frac{{F}_{b}}{{F}_{a}}{2}^{{E}_{b}-{E}_{a}}{Q}_{b}+\frac{{B}_{b}-{B}_{a}}{{F}_{a}}{2}^{-{E}_{a}}\\ ={F}_{s}{2}^{{E}_{b}-{E}_{a}}{Q}_{b}+{B}_{net},\end{array}$$

where *F _{s}* is
the adjusted fractional slope and

Both *F _{s}* and

The remaining conversions and operations are performed *online* by
the fixed-point processor, and depend on the slopes and biases for
the input and output data types. The conversions and operations are
given by these steps:

The initial value for

*Q*is given by the net bias,_{a}*B*:_{net}$${Q}_{a}={B}_{net}.$$

The input integer value,

*Q*, is multiplied by the adjusted slope,_{b}*F*:_{s}$${Q}_{RawProduct}={F}_{s}{Q}_{b}.$$

The result of step 2 is converted to the modified output data type where the slope is one and bias is zero:

$${Q}_{Temp}=convert\left({Q}_{RawProduct}\right).$$

This conversion includes any necessary bit shifting, rounding, or overflow handling.

The summation operation is performed:

$${Q}_{a}={Q}_{Temp}+{Q}_{a}.$$

This summation includes any necessary overflow handling.

Note that the maximum number of conversions and operations is performed when the slopes and biases of the input signal and output signal differ (are mismatched). If the scaling of these signals is identical (matched), the number of operations is reduced from the worst (most inefficient) case. For example, when an input has the same fractional slope and bias as the output, only step 3 is required:

$${Q}_{a}=convert\left({Q}_{b}\right).$$

Exclusive use of binary-point-only scaling for both input signals and output signals is a common way to eliminate mismatched slopes and biases, and results in the most efficient simulations and generated code.

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