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:
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.
