SoC
  • Home
  • Arm
  • Arm Cortex M0/M0+
  • Arm Cortex M4
  • Arm Cortex M3
  • Contact
Reading: Tips for Using the FPU on Cortex-M4 Efficiently
SUBSCRIBE
SoCSoC
Font ResizerAa
  • Home
  • Arm
  • Arm Cortex M0/M0+
  • Arm Cortex M4
Search
  • Home
  • Arm
  • Arm Cortex M0/M0+
  • Arm Cortex M4
Have an existing account? Sign In
Follow US
  • Looking for Something?
  • Privacy Policy
  • About Us
  • Sitemap
  • Contact Us
© S-O-C.ORG, All Rights Reserved.
Arm Cortex M4

Tips for Using the FPU on Cortex-M4 Efficiently

Graham Kruk
Last updated: October 5, 2023 10:08 am
Graham Kruk 8 Min Read
Share
SHARE

The Cortex-M4 processor includes a single precision floating point unit (FPU) that can significantly improve the performance of math-intensive code. However, to utilize the FPU efficiently requires some care in coding and optimization. Here are some tips for getting the most out of the Cortex-M4 FPU:

Contents
Enable the FPU in Your ToolchainUse Hardware Floating Point TypesUse Floating Point Libraries for Common FunctionsAvoid Conversions Between Float and IntegerMinimize Data Transfer Between FPU and General RegistersOptimize Memory Layout for Floating Point DataUse Floating Point Constants Rather Than CalculationsAvoid DenormalsUse Fast Math LibrariesEnable Floating Point Optimization in CompilerProfile Your CodeMeasure Both Speed and PrecisionLearn AssemblyUse an FPU-Optimized Cortex-M4 ChipConsider Using Double PrecisionUse Hardware DivideManage Floating Point Context SwitchingUse SIMD InstructionsOptimize Across Module BoundariesConsider Hardware AcceleratorsTune Compiler Settings Per-FunctionUnroll Small LoopsTry Different Data LayoutsReduce Code SizeUse Fast Math When PossibleSimplify Math OperationsReduce Float to Integer to Float Conversions

Enable the FPU in Your Toolchain

The first step is to enable the FPU in your toolchain. In the compiler settings, enable the -mfloat-abi=hard or -mfloat-abi=softfp option. This will generate floating point instructions rather than software emulation libraries. You may also need to enable FPU support in the linker script.

Use Hardware Floating Point Types

Use the float and double types in your code rather than software emulated types like float_t. The compiler will generate hardware floating point instructions for the hardware types. For example: float foo(float x) { return x * 1.5f; }

Use Floating Point Libraries for Common Functions

The C standard library includes optimized floating point functions for common operations like sin, cos, sqrt, exp, log, pow, etc. Using these will be faster than coding your own or using a software math library. #include float bar(float x) { return sqrtf(x); }

Avoid Conversions Between Float and Integer

Conversions between float and integer types require moves between the FPU and general purpose registers. This can add overhead. If possible, use floating point for all calculations. int sum(float arr[], int n) { float result = 0.0f; for (int i = 0; i < n; i++) { result += arr[i]; // Better to accumulate in float } return (int)result; // Avoid this conversion }

Minimize Data Transfer Between FPU and General Registers

Transferring data between the FPU and general purpose registers requires store and load instructions which can reduce performance. Structure your algorithms to minimize movement of data in and out of the FPU. void process(float *arr, int n) { float acc = 0.0f; for (int i = 0; i < n; i++) { acc += arr[i]; // Do all ops in FPU } print(acc); // Only one move to print result }

Optimize Memory Layout for Floating Point Data

The Cortex-M4 can perform single cycle float load/store from aligned memory. Use 32-bit alignment for float variables and arrays. Also avoid false sharing between float and non-float data. struct { float x; int y; } foo; // Bad alignment struct { char c; float d[10]; int e; } bar; // Poor cache behavior float x; // 32-bit align struct { float a; float b; } baz; // Good alignment

Use Floating Point Constants Rather Than Calculations

Using floating point constants will be faster than calculating them. The compiler can optimize constants. area = radius * radius * 3.14159; // Slower area = radius * radius * M_PI; // Faster

Avoid Denormals

Denormalized floating point numbers require more processing. Initialize variables to avoid denormals. float x; // May be denormal float x = 0.0f // Avoids denormal

Use Fast Math Libraries

Some math libraries like ARM’s CMSIS DSP are optimized specifically for Cortex-M4 and FPU performance. Using DSP functions can be much faster than standard C math library. #include “arm_math.h” arm_sqrt_f32(x); // Faster square root

Enable Floating Point Optimization in Compiler

Make sure to enable floating point optimizations in your compiler settings. This will produce better code generation for floating point intensive code. -O3 -ffast-math // Good optimization options

Profile Your Code

Profile and benchmark different implementations to identify where the hotspots are. Focus optimizations on frequently executed floating point code to get the best performance gains.

Measure Both Speed and Precision

More optimization can reduce precision. Make sure the optimizations do not negatively impact the precision required by your application.

Learn Assembly

Understanding ARM assembly and the floating point instructions can help with manual optimizations in critical code sections.

Use an FPU-Optimized Cortex-M4 Chip

Some Cortex-M4 chips are specifically optimized for floating point performance with features like fast FPU context switching. Choose the right MCU fit for your application.

Consider Using Double Precision

While double precision is slower, it may give better precision for algorithms sensitive to round-off errors. Profile where the extra precision is needed.

Use Hardware Divide

The Cortex-M4 includes a hardware divide unit for integers. Use integer divide rather than floating point when possible. int x = a / b; // Fast hardware divide float y = (float)a / (float)b; // Slower floating point divide

Manage Floating Point Context Switching

The FPU has extra registers that may require context switches with multi-threaded code. Understand the overhead of context switches.

Use SIMD Instructions

The Cortex-M4 includes SIMD instructions for parallel arithmetic. Using NEON SIMD intrinsics can accelerate some algorithms. float32x4_t v = vaddq_f32(a, b); // SIMD add

Optimize Across Module Boundaries

Compiler optimizations work within a module or file. Profile and optimize across module calls for maximum performance.

Consider Hardware Accelerators

Some companies provide optimized hardware accelerators for floating point algorithms. These can offload intense computations from the Cortex-M4.

Tune Compiler Settings Per-Function

You can use pragma directives in the code to apply optimizations to specific functions rather than globally. #pragma optimize(“O3”) void myFunc() { … }

Unroll Small Loops

Unrolling small loops can reduce overhead of the loop counter and branch. But beware code size increase.

Try Different Data Layouts

Structure of Arrays vs Array of Structures layouts can perform differently. Test different organizations.

Reduce Code Size

More compact code better fits in caches and reduces instruction fetches. Optimize speed without blowing up code size.

Use Fast Math When Possible

Fast math compiler options trade precision for speed. Use when precision requirements allow it.

Simplify Math Operations

Simple mathematical tricks can sometimes replace slow operations. For example, use bit shifts instead ofdivide/multiply.

Reduce Float to Integer to Float Conversions

Converting between float and int types has overhead. See if you can restructure algorithms to avoid conversions.

By following these tips, you can take better advantage of the Cortex-M4 FPU to create faster and more efficient floating point code while avoiding common performance pitfalls.

Newsletter Form (#3)

More ARM insights right in your inbox

 


Share This Article
Facebook Twitter Email Copy Link Print
Previous Article When to Use Intrinsics vs Assembler for Math Functions on Cortex-M4?
Next Article Reducing Context Switch Overhead with FPU Registers on Cortex-M4
Leave a comment Leave a comment

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

2k Followers Like
3k Followers Follow
10.1k Followers Pin
- Sponsored-
Ad image

You Might Also Like

Does arm cortex-M4 have stages of pipeline?

The Cortex-M4 processor from ARM does have a pipeline structure…

12 Min Read

Reducing Context Switch Overhead with FPU Registers on Cortex-M4

The Cortex-M4 processor includes a floating point unit (FPU) to…

7 Min Read

Understanding Pipeline Hazards in Cortex-M4 Microcontrollers

The Cortex-M4 processor implements a 3-stage pipeline to improve performance…

8 Min Read

Cortex M4 Write Buffer Explained

The Cortex-M4 processor includes a write buffer to improve performance…

16 Min Read
SoCSoC
  • Looking for Something?
  • Privacy Policy
  • About Us
  • Sitemap
  • Contact Us
Welcome Back!

Sign in to your account