The ARM Neon technology is a SIMD (Single Instruction Multiple Data) architecture extension to the ARM Cortex-A series of CPUs. It provides vector processing capabilities that allow a single instruction to perform the same operation on multiple data points simultaneously.
The key components that make up the Neon architecture are:
Neon Register File
The Neon register file consists of 32 64-bit wide registers that can hold 8, 16, 32 or 64 bit data types. This allows more data to be processed per instruction compared to regular 32-bit ARM registers.
Neon Load/Store Units
Dedicated load/store units are provided for Neon registers to transfer data efficiently between main memory and the SIMD processing units. This includes instructions for aligned and unaligned data access.
Neon Execution Units
The Neon execution units include:
- Integer ALUs for arithmetic and bitwise operations on integer vectors
- Floating point units for arithmetic operations on floating point vectors
- Polynomial multiply units for specialized DSP algorithms
With multiple identical execution units, Neon can perform SIMD processing on vectors by applying the same operation across all data elements concurrently.
Neon Instruction Set
The Neon instruction set provides vector processing capabilities ranging from arithmetic, logical, load/store to SIMD specific instructions like rounding, saturation etc. Key capabilities include:
- Arithmetic operations on integer, floating point and polynomial vectors
- Logical and shift operations on integer vectors
- Data type conversion and movement between vectors
- Load/Store for aligned and unaligned vector data access
- Specialized instructions like rounding, saturation for DSP use cases
The quadword is the native vector size used by Neon architecture. A quadword consists of 128 bits which can hold various data types:
- 16 x 8-bit integers
- 8 x 16-bit integers
- 4 x 32-bit integers
- 2 x 64-bit integers
- 4 x 32-bit floating points
- 2 x 64-bit floating points
Neon instructions can operate on quadwords in SIMD fashion to accelerate media and signal processing workloads.
Neon supports a rich set of vector datatypes that include:
- Integer – Signed and unsigned integers of 8, 16, 32, 64 bit sizes
- Floating point – 32-bit single precision and 64-bit double precision
- Polynomial – For specialized DSP algorithms
Operations can be performed across elements within a vector regardless of the element datatype.
Neon enables parallel SIMD processing by applying a single instruction on multiple data elements concurrently. For example, a vector addition on four 32-bit integers can be performed in a single instruction.
This is faster than executing 4 separate scalar addition instructions. SIMD parallelism boosts performance for suitable workloads like media processing, graphics, DSP, ML inference etc.
Neon vs ARM Vector Extension (SVE)
SVE is a more advanced SIMD architecture available on newer ARMv8 CPUs while Neon is supported across most ARM Cortex-A profile devices.
Key differences between Neon and SVE:
- Neon has fixed 128-bit vector size while SVE has configurable vector lengths
- SVE has significantly more vector registers compared to Neon
- Neon supports integer, floating point and polynomial datatypes while SVE only supports integers
- SVE provides greater scalability and performance for large vector operations
In summary, the ARM Neon technology provides SIMD capabilities to Cortex-A series CPUs through dedicated vector registers, execution units and instruction set extensions. Its quadword vector size, rich datatypes and SIMD processing boost performance for workloads like media, signal processing, ML inference etc. While more advanced, SVE is compatible with and complementary to Neon in newest ARM CPUs.
To utilize Neon capabilities, ARM CPUs provide interfaces to program Neon vectors using various languages and APIs:
The Neon instruction set can be directly used via ARM assembly language programming. This allows precise control of the CPU but is complex for larger applications.
Compilers like gcc and clang provide Neon intrinsics that map to Neon assembly instructions using C/C++ functions. This makes Neon programming easier in C/C++ code.
Higher level NEON accelerated APIs are provided by ARM for certain domains like image processing, computer vision, machine learning etc. Eg: ARM Compute Library, CMSIS-NN.
Compilers can auto-vectorize code using Neon by detecting SIMD parallelism opportunities. However, this may require annotations using pragmas.
So Neon can be programmed using inline assembly, intrinsics, accelerated libraries and auto-vectorization. This allows tapping into Neon performance benefits across various languages and use cases.
Some key use cases where Neon SIMD capabilities provide performance benefits:
Media & Signal Processing
Neon accelerates audio, video and image processing workloads like encoders/decoders, filters, computer vision etc that involve large datasets.
Vector computations involved in simulations, modeling, engineering software etc can leverage Neon for acceleration.
Neural network inference using technologies like TensorFlow Lite relies heavily on Neon performance on ARM devices.
Graphics operations like shaders, geometry, physics etc involve vector math that can be offloaded to Neon engines.
Encryption algorithms using techniques like AES, SHA etc involve bitwise operations well suited for Neon.
Neon also finds use in workloads involving digital signal processing, big data analytics, compression, databases and more. ARM CPU implementations optimize Neon engines targeting different application domains.
The ARM Neon SIMD architecture provides significant performance benefits for workloads involving media processing, analytics, ML inference etc. Its quadword vector size, register file, execution units and instruction set power, efficient parallel processing in ARM Cortex-A CPUs. With growing demands in domains like AI/ML, computer vision and 5G, expect Neon capabilities to expand to 1024-bit vectors and beyond in future ARM chip designs.