What is Single Instruction Multiple Data (SIMD) in ARM Neon?

SIMD (Single Instruction Multiple Data) refers to a type of parallel processing where a single instruction can operate on multiple data elements simultaneously. This allows the same operation to be performed on multiple data points in one go, which can significantly speed up processing compared to doing the operations sequentially.

ARM Neon is ARM’s implementation of SIMD instructions in their Cortex-A series and Cortex-R series of CPUs. Neon provides SIMD capabilities to ARM processors, allowing them to process multiple data elements using a single instruction. This improves performance for workloads like image processing, audio/video encoding/decoding, cryptography, and more.

How Does SIMD in ARM Neon Work?

Neon SIMD works by loading multiple data values into larger register sizes, then applying the operation to the entire register at once. For example:

• Normal registers in ARM are 32 bits wide.
• Neon extends this with 64-bit doubleword registers, 128-bit quadword registers, and longer registers.
• These wider registers can hold multiple 32-bit values or smaller data types like 8-bit ints.
• SIMD instructions apply the operation to the full width of the registers.

So if a 128-bit quadword register holds 4 x 32-bit values, an add operation will add all 4 values at once. This is much faster than doing 4 separate add instructions.

Key SIMD Features in ARM Neon

Here are some of the key capabilities Neon adds for SIMD processing:

• Wider vector registers – As mentioned above, Neon provides 64-bit, 128-bit and longer registers to hold multiple data values.
• Data types – Neon supports various integer and floating point data types like 8-bit ints, 16-bit floats, 32-bit ints, and 64-bit doubles. Multiple elements of these types can be packed into the wider registers.
• Vector instructions – Neon includes a wide set of SIMD vector instructions for arithmetic, logical, load/store, multiplication, fused multiply-add, etc. These work on the full width registers.
• Vector addressing modes – To load/store vector registers efficiently, Neon provides flexible addressing modes like post-increment, pre-decrement, strided access, etc.
• Vector permutations – Neon can also transpose or reorder vector register contents to enable flexible data access patterns.

Benefits of Neon SIMD

Adding SIMD capabilities provides significant performance benefits for suitable workloads on ARM chips:

• Doing operations as vectors instead of scalars can provide 4x, 8x or more speedup. For example, a 128-bit SIMD add does 4 x 32-bit adds together.
• Highly parallel workloads like multimedia, imaging, signal processing, physics simulations etc can utilize SIMD to do more work per cycle.
• SIMD accelerates repetitive computations on arrays or matrices by applying operations to multiple data points concurrently.
• Advanced compiler auto-vectorization can convert scalar code to use SIMD automatically.
• Explicit programming with Neon intrinsics or assembly can optimize performance critical code segments.
• SIMD boosts efficiency and throughput for data parallel segments without requiring more software threads.

Programming with Neon SIMD

Neon can be programmed via:

• Intrinsics – C/C++ functions that map to Neon instructions while retaining portability.
• Assembly – Directly using Neon SIMD assembly language instructions.
• Compiler auto-vectorization – Compilers like GCC can auto-vectorize code with SIMD instructions.
• SIMD libraries – Libraries like OpenCV utilize Neon SIMD internally to speed up operations.

Intrinsics give developers Neon access in high level C/C++ code without writing full assembly. Common examples include:

• Vector load/store – vld1q_f32(), vst1q_u8()
• Arithmetic – vaddq_s32(), vmulq_f32()
• Logical – vandq_u16(), vorrq_u8()
• Comparison – vcgtq_f32(), vcleq_s8()
• Permute/zip – vzipq_u8(), vrev64q_s16()

Assembly provides the lowest level access to Neon but requires more programmer effort. The compiler auto-vectorization approach provides a compromise using existing scalar code.

Neon SIMD Architectural Details

Some key details on the Neon SIMD engine in ARM processors include:

• Neon is a 128-bit wide SIMD coprocessor tightly integrated with the ARM register file.
• It has a pipelined datapath and arithmetic logical units separate from the main ARM core to enable independent SIMD execution.
• Quadword registers provide 128-bit vectors that can fit 4x 32-bit floats or various other datatype combinations.
• Supports extensive vector instruction set – arithmetic, logical, permute, compare, shift, convert, load/store, etc.
• Load/store units allow gathering vectors from non-contiguous memory addresses.
• Low latency interface enables high data transfer rates between Neon and ARM core registers.

This combination of wide quadword registers, dedicated execution pipes, and a large vector instruction set allows Neon to deliver flexible and efficient SIMD computation enhancing overall ARM processor performance.

Use Cases and Applications

Some common applications that benefit from Neon SIMD acceleration are:

• Image processing – Filters, effects, edits applied to batches of pixel data.
• Neural networks – Matrix math and parallel computations.
• Computer vision – Feature detection, image recognition.
• Video encoding/decoding – Performing compression/decompression and format conversion.
• Audio processing – Applying filters or effects to audio samples.
• Speech recognition – Analyzing voice data and converting speech to text.
• Scientific computing – Data analysis, simulations with large datasets.
• Computer graphics – Rendering 3D scenes and processing shader programs.
• Cryptography – Encryption, decryption, hashing.
• Financial analysis – Running models on market datasets.

Any application that works on streams of similar data elements can benefit from Neon’s ability to apply operations to multiple data points concurrently.

Conclusion

ARM Neon introduces SIMD capabilities to ARM processors, providing significant performance improvements through vector processing. Packing data into larger registers and executing operations on vectors instead of scalars boosts throughput for suitable parallel workloads. Neon offers programmers intrinsics and assembly language access to utilize its capabilities fully. Leading-edge mobile SoCs integrate Neon to accelerate multimedia, imaging, AI, ML, scientific workloads, and more on ARM-based devices.