Decrement Real World

R2026b

Decrease real-world value of signal by one

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Decrement Real World block

To add a block to a model, double-click the canvas and start typing the block name. Then, select the block from the list.

Libraries:
Simulink / Additional Math & Discrete / Additional Math: Increment - Decrement
HDL Coder / Math Operations

Description

The Decrement Real World block decreases the real-world value of the signal by one. Overflows always wrap.

Examples

Increment and Decrement Real-World Values

This example shows how to increase and decrease the real-world value of a signal using the following blocks:

• Increment Real World

• Decrement Real World

• Decrement Time To Zero

• Decrement To Zero

The Scope block shows the output of a Sine Wave block with amplitude 5, as well as the real-world value of that signal incremented and decremented by one.

The Scope1 block shows the output of a Sine Wave block with amplitude 3, as well as the output of the Decrement To Zero and Decrement Time To Zero blocks:

• The Decrement To Zero block decreases the input sine wave signal by one, and ensures the value never goes below zero.

• The Decrement Time To Zero block decreases the input sine wave signal by the sample time, Ts, and ensures that the value never goes below zero.

Ports

Input

Port_1 — Input signal

scalar | vector | matrix

Input signal, specified as a scalar, vector, or matrix.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | fixed point

Output

Port_1 — Output signal

scalar | vector | matrix

Output is the real-world value of the input signal decreased by one. Overflows always wrap. The output has the same data type and dimensions as the input.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | fixed point

Block Characteristics

Data Types	double | fixed point | integer | single
Direct Feedthrough	no
Multidimensional Signals	no
Variable-Size Signals	yes
Zero-Crossing Detection	no

Extended Capabilities

C/C++ Code Generation

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

The code generator does not explicitly group primitive blocks that constitute a nonatomic masked subsystem block in the generated code. This flexibility allows for more efficient code generation. In certain cases, you can achieve grouping by configuring the masked subsystem block to execute as an atomic unit by selecting the Treat as atomic unit option.

HDL Code Generation

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

HDL Coder™ provides additional configuration options that affect HDL implementation and synthesized logic.

HDL Architecture

This block has one default HDL architecture.

HDL Block Properties

General

HDL Property	Description
ConstrainedOutputPipeline	Number of registers to place at the outputs by moving existing delays in the design. Distributed pipelining does not redistribute these registers. The default value is 0. For more details, see ConstrainedOutputPipeline (HDL Coder).
InputPipeline	Number of input pipeline stages to insert in the generated code. Distributed pipelining and constrained output pipelining can move these registers. The default value is 0. For more details, see InputPipeline (HDL Coder).
OutputPipeline	Number of output pipeline stages to insert in the generated code. Distributed pipelining and constrained output pipelining can move these registers. The default value is 0. For more details, see OutputPipeline (HDL Coder).
SynthesisAttributes	Specifies the synthesis attributes for the blocks and block output signals in the model. The generated HDL code contains these attributes. For more information, see SynthesisAttributes (HDL Coder).

Data Type Support

This table lists the data types that the input port supports for HDL code generation:

Input Port	Dimensions	Fixed-Point Values	Floating-Point Values	Built-in Integers	Bus Signals	Boolean Values	Complex Values
Port_1	Scalar Vector Matrices up to two dimensions	Yes	Half Single Double	Yes	No	No	No

To generate HDL code using native floating-point input data types, open the HDL Code Generation > Floating-Point pane in the Configuration Parameters dialog box and select the Use floating-point parameter. For more information, see Generate Target-Independent HDL Code with Native Floating-Point (HDL Coder).

Native Floating-Point Latency

When you use half, single, or double data types as inputs to the block, you can use the Latency Strategy (HDL Coder) model configuration parameter to specify whether the blocks latency maps to minimum or maximum latency values when using native floating-point libraries. The block supports these native floating-point latency values:

Floating-Point Data Type	Latency Strategy Setting	Latency Cycles	Custom Latency Support
double	MIN	6	No
	MAX	11
single	MIN	6	No
	MAX	11
half	MIN	4	No
	MAX	8

The block uses multicycle implementations to introduce additional latency in the generated code. To see the added latency, view the generated model or validation model. See Generated Model and Validation Model (HDL Coder).

Optimizations

You can use these HDL Coder optimizations to optimize the speed, area, and I/Os.

Area Optimization

Optimization	Description
Resource Sharing (HDL Coder)	Resource sharing is an area optimization in which HDL Coder identifies multiple functionally equivalent resources and replaces them with a single resource.
Streaming (HDL Coder)	Streaming is an area optimization in which HDL Coder transforms a vector data path to a scalar data path or to several smaller-sized vector data paths.

Speed Optimization

Optimization	Description
Specify Distributed Pipelining Settings (HDL Coder)	Distributed pipelining, or register retiming, is a speed optimization that moves existing delays in a design to reduce the critical path while preserving functional behavior.
Clock-Rate Pipelining (HDL Coder)	Clock-rate pipelining is an optimization framework that allows other speed and area optimizations to introduce latency at the clock rate.
Specify Adaptive Pipelining Settings (HDL Coder)	Adaptive pipelining optimization creates patterns or combination of blocks with registers that can improve the achievable clock frequency and reduce the area usage on the FPGA boards by inserting pipeline registers to the blocks in your design.
Critical Path Estimation (HDL Coder)	To quickly identify the most likely critical path in your design, use critical path estimation. Critical path estimation speeds up the iterative process of finding the critical path. To know blocks that are characterized in critical path estimation, see Characterized Blocks (HDL Coder).

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

Version History

Introduced before R2006a

R2026b: Generate HDL code when using native floating-point and optimizations

You can now generate synthesizable HDL code for the Decrement Real World block when using native floating-point data types, including half, single, or double.

You can also use various HDL optimizations, such as distributed pipelining, hierarchy flattening, or delay balancing to generate optimized HDL code for the block.

R2026a: Specify synthesis attributes for blocks and block output signals

Use the SynthesisAttributes HDL block property to specify the synthesis attributes for the block and its output signals. HDL Coder includes these attributes in the generated HDL code.