Understand the Hardware Limitations
When working with low-power embedded devices, understanding the hardware capabilities is crucial. Floating-point operations are typically slower and more power-consuming than integer calculations, especially in microcontrollers without a floating-point unit (FPU). Before diving into optimization, ensure that your hardware supports floating-point operations efficiently. If it lacks an FPU, consider using fixed-point arithmetic for critical performance sections to conserve power.
Use Fixed-Point Arithmetic
Fixed-point arithmetic can be a more efficient alternative on devices without an FPU. By representing decimal numbers using integers, you can perform calculations with less overhead.
// Example of fixed-point arithmetic
#define FIXED_POINT_SCALE 1000
int fixed_multiply(int a, int b) {
return (a * b) / FIXED_POINT_SCALE;
}
int fixed_divide(int a, int b) {
return (a * FIXED_POINT_SCALE) / b;
}
This approach sacrifices some precision, so it’s ideal in scenarios where high precision is not critical. Carefully select the scale factor based on the precision needs of your application.
Optimize Compiler Settings
Examine and tweak compiler settings to generate more efficient code. Most compilers for embedded systems have optimization flags that can enhance floating-point performance. For instance, using the -O2 or -O3 optimization levels.
- In GCC, use
-ffast-math to enable optimizations that can increase floating-point operation performance.
- On ARM Cortex-M processors, enabling the FPU with the
-mfpu flag and setting the correct floating-point ABI with -mfloat-abi=hard can be beneficial.
Utilize Precomputed Tables
For operations like trigonometric calculations or logarithms, use precomputed lookup tables. This can significantly reduce the computation time at the cost of additional memory usage.
// Example: Sine lookup table
static const float sin_table[90] = {
// Precomputed sine values from 0 to 89 degrees
0.0, 0.017452, 0.034899, /* etc. */
};
float fast_sin(int angle) {
while (angle < 0) angle += 360;
angle = angle % 360;
if (angle <= 90) return sin_table[angle];
if (angle <= 180) return sin_table[180 - angle];
if (angle <= 270) return -sin_table[angle - 180];
return -sin_table[360 - angle];
}
Always validate that the memory overhead from lookup tables is acceptable for your system's constraints.
Minimize Floating-Point Operations
Reduce the number of floating-point calculations by reordering operations or using integer approximations when possible. Avoid operations inside loops if they can be computed outside. For instance, don't repeatedly compute constants within a loop.
Use Efficient Libraries
Leverage optimized libraries for mathematical operations. Libraries created specifically for embedded systems often include hand-tuned assembly implementations for crucial functions which can outperform generic C functions.
- CMSIS-DSP in ARM Cortex-M devices provides an optimized DSP library with some floating-point support.
By utilizing these practical approaches, you can make floating-point operations more efficient in embedded C applications, optimizing both performance and power consumption. Always profile your code to understand the impact of these changes in your specific environment.
