Half-precision (HP) floating-point instructions in Arm Cortex-M series processors provide support for calculations using 16-bit floating-point data types. This allows Cortex-M processors to perform high-performance computing workloads that involve large amounts of floating-point math, while reducing power consumption and memory footprint compared to single-precision calculations.
Overview of Half-Precision Floating-Point
Floating-point numbers are used to represent real numbers in computing, like 1.23 or 3.141592. Single-precision floating-point uses 32 bits to store a number, while half-precision uses only 16 bits. The trade-off is less precision for more compact storage and faster processing.
The IEEE 754 standard defines a 16-bit floating-point format called binary16 or FP16. It has a 5-bit exponent, 10-bit mantissa, and 1 sign bit. This allows for a range of 65,504 distinct values to be represented. HP floating-point is useful for applications like machine learning, image processing, and scientific computing where high precision is not always critical.
HP Floating-Point Support in Arm Cortex-M
Many Arm Cortex-M series chips now include optional extensions to support half-precision floating-point instructions. These include:
- Cortex-M4 – Provides basic HP operation support with the FP extension
- Cortex-M7 – Adds fused multiply-add HP instructions with the FP extension
- Cortex-M33 – Includes full HP math capability with the Helium extension
- Cortex-M35P – Optimized for processing HP data with up to 64 MACs
The half-precision extensions add new registers, data types, and arithmetic instructions specifically for 16-bit floats. This hardware acceleration allows Cortex-M chips to efficiently work with HP data.
Benefits of Using Half-Precision
Here are some of the major benefits of leveraging half-precision floating-point support in Arm Cortex-M processors:
- Reduced Memory Footprint – HP floats use half the storage of single-precision. This allows more data to fit in memory caches and decreases pressure on memory bandwidth.
- Faster Computation – More HP data can be loaded per instruction. Combined with specialized hardware, this speeds up floating-point computation.
- Lower Power Consumption – Less memory traffic and optimized HP data paths result in greater energy efficiency for floating-point workloads.
- Better Performance per Area – Packing in more HP MAC units lets Cortex-M chips achieve more FLOPs without significantly increasing die size.
For applications like machine learning inferencing, the reduced precision of FP16 is often sufficient. Arm Cortex-M HP support allows high performance at low power budgets.
Programming with Half-Precision
To take advantage of half-precision floating-point, Cortex-M code needs to be written using the __fp16 data type and HP instructions. This involves:
- Declaring variables and arrays with __fp16 instead of float or double.
- Using explicit type conversion between __fp16 and float when needed.
- Calling HP vector and matrix math functions from supported math libraries.
- Using HP intrinsic functions to inline optimized FP16 code.
- Setting compiler options like -mfp16-format to enable generation of HP instructions.
Proper use of HP data types and operations allows the compiler to produce very efficient code that maximizes the capabilities of the Cortex-M processor. For machine learning applications, common numeric libraries like CMSIS-NN have added support for FP16 data types and inputs.
Hardware Considerations
There are some limitations to keep in mind when working with half-precision floating-point on Cortex-M:
- Not all Cortex-M variants have HP extension support. Need to select a model with FP16 capability.
- Watch out for precision loss in computations. May require retaining intermediate values at higher precision.
- Code optimized for HP math may suffer degraded performance on Cortex-M CPUs without specific FP16 hardware.
- Applications requiring high accuracy may still need single-precision, especially for accumulating values.
Proper testing and profiling is important to ensure the use of HP floats provides the expected benefits and does not introduce issues due to lower precision. Gradual conversion of key computation kernels can help evaluate the impact on application accuracy.
Conclusion
In summary, half-precision floating-point support provides Arm Cortex-M series microcontrollers with an efficient way to boost performance for workloads involving floating-point math. When used properly, the FP16 capabilities of Cortex-M processors can speed up computation, reduce memory usage, lower power draw, and enable high compute density for applications like machine learning inferencing. Developers building software for Cortex-M systems should evaluate if leveraging FP16 types and operations makes sense for their specific use case needs.
