Digital Signal Processing (DSP) refers to processing digital signals using specialized programming and hardware optimizations in ARM processors. DSP enhances ARM processors to efficiently execute signal processing algorithms requiring extensive mathematical computations on streaming data in real-time. DSP capabilities are critical in ARM devices for applications like audio/video processing, speech recognition, image processing, 5G and other wireless communications, IoT sensor analytics, radar processing, and more.
DSP Extensions in ARM
ARM processors include DSP extensions to accelerate performance of signal processing workloads. Some key DSP extensions in ARM include:
- SIMD (Single Instruction Multiple Data) – Performs same operation on multiple data points using a single instruction.
- NEON – 128-bit SIMD engine for accelerating media and signal processing.
- SVE (Scalable Vector Extension) – Vector processing up to 2048 bits for high performance computing.
- Crypto extensions – Hardware accelerators for cryptographic algorithms and security.
- DSP instructions – Specialized instructions to optimize filtering, transforms, correlation, etc.
These DSP optimizations are implemented directly into the ARM processing pipeline allowing parallel execution of signal processing tasks. Advanced ARM processor families like Cortex-A and Cortex-M integrate DSP extensions customized for embedded and real-time applications.
NEON SIMD Engine
NEON is a key SIMD engine in ARM processors focused on accelerating media and signal processing performance. It extends the ARM architecture with a 128-bit vector processing unit and instruction set. NEON enables ARM cores to perform multiple DSP-type operations in parallel on a single instruction.
NEON provides instructions for:
- Integer and floating point arithmetic
- Matrix operations
- Filtering and convolutions
- Audio/video processing
- Image enhancement
- Speech recognition
- Cryptography
- Machine learning inferencing
By processing 128-bit vectors in a single cycle, NEON offers up to 4X higher DSP throughput over ARM cores alone. NEON is integrated along with ARM cores in application processors like Cortex-A series targeting high performance computing.
DSP Instructions
ARM processors include a set of DSP instructions to accelerate specialized operations commonly used in signal processing algorithms. These include instructions for:
- Saturating Arithmetic – Saturates results to maximum/minimum values to avoid overflow.
- Multiplication Accumulation (MAC) – Performed in a single cycle for filters/transforms.
- Circular Buffer Addressing – Efficient address generation for streaming buffers.
- Single Instruction Multiple Data (SIMD) – Parallel data processing.
- Parallel Bit Manipulation – Fast bit operations on vectors.
DSP instructions are deeply integrated into the ARM datapath to enable back-to-back execution with minimal stalls. This allows ARM processors to achieve high DSP throughput at optimal energy efficiency.
DSP Software Support
To fully utilize DSP capabilities in ARM, software support is needed for programming and libraries:
- Intrinsics – Language extensions to directly access DSP instructions through compilers.
- DSP libraries – Highly optimized DSP functions for common algorithms.
- DSP-enhanced OSes – Real-time operating systems like FreeRTOS with DSP support.
- Frameworks – Media frameworks like OpenCV optimized for ARM’s DSP extensions.
Software support enables developers to efficiently program ARM’s DSP features without directly coding assembly. This allows rapid development of high performance DSP applications on ARM platforms.
DSP Workloads and Use Cases
Here are some common workloads and use cases that leverage DSP capabilities in ARM processors:
- Audio Processing – Echo cancellation, noise suppression, encoding/decoding etc.
- Speech Recognition – Trigger word detection, speech-to-text.
- Image Processing – Filters, transformations, segmentation, computer vision.
- Video Processing – Encoding/decoding, image enhancement, analytics.
- Wireless Modems – Radio signal processing, channel encoding/decoding.
- Radar Processing – Object detection, motion tracking, collision avoidance.
- Predictive Maintenance – Signal analysis on vibration, sound, temperature data.
- IoT/Edge ML Inferencing – Neural network inferencing on sensor data.
DSP accelerates these workloads in ARM devices used across end markets like mobile, IoT, automotive, industrial automation, healthcare and more. DSP unlocks new capabilities like computer vision, speech interfaces and predictive analytics on ARM’s energy efficient architecture.
DSP Benchmarking on ARM
DSP performance on ARM can be measured using standardized benchmarks like:
- EEMBC – Workloads for audio, imaging, computer vision.
- BDTI – Signal processing and machine learning.
- MLPerf – Machine learning inferencing benchmarks.
- SPEC – Standard Performance Evaluation Corporation benchmarks.
Vendors optimize ARM systems on these benchmarks to highlight DSP throughput, latency and power efficiency. DSP acceleration is a competitive factor driving roadmaps and offerings of ARM processors.
Conclusion
DSP capabilities are critical for ARM processors to deliver high performance real-time signal processing required in modern embedded and edge devices. Architectural optimizations like NEON and DSP instructions accelerate workloads like audio, imaging, computer vision, machine learning inferencing, wireless modem processing and radar. DSP will continue to be a key factor driving adoption of ARM processors across mobile, IoT, automotive, and other markets where signal processing capabilities are required in low power envelopes.
