Optimizing DPD Algorithm with ARM NEON SIMD

Project Overview

This project focused on significantly improving the performance of Digital Predistortion (DPD) algorithm implementation through ARM NEON SIMD optimization. The optimization resulted in substantial performance gains by leveraging parallel processing capabilities of ARM architecture.

Technical Background

Digital Predistortion (DPD)

Digital Predistortion is a crucial technique in modern wireless communication systems that compensates for power amplifier (PA) nonlinearities. The process works by:

1. Pre-analyzing the PA’s nonlinear characteristics
2. Applying inverse distortion to the input signal
3. Achieving linear output after PA processing

Figure 1: Digital Predistortion working principle showing signal transformation stages

ARM NEON Architecture

NEON is ARM’s advanced SIMD (Single Instruction Multiple Data) architecture extension, designed for high-performance computing applications. Key features include:

• 32 dedicated 128-bit SIMD registers
• Parallel processing capabilities
• Support for both integer and floating-point operations

Figure 2: NEON SIMD parallel processing visualization

Optimization Strategy

1. Memory Alignment and Data Structure Optimization

To maximize NEON register utilization, careful attention was paid to data alignment and structure:

• Memory Alignment: Ensured 128-bit alignment for optimal NEON register loading
• Complex Data Handling: Separated real and imaginary components for efficient SIMD processing
• Contiguous Memory Layout: Optimized data placement for vectorized operations

Figure 3: Complex data separation and alignment strategy

2. Advanced Loop Optimization

Implemented sophisticated loop optimization techniques:

• Loop Unrolling: Reduced branch predictions and improved instruction pipeline efficiency
• Loop Unwinding: Maximized register utilization and reduced memory access overhead
• Register Allocation: Optimized temporary result storage in NEON registers

Figure 4: Loop unrolling and unwinding implementation

3. Matrix Operation Enhancement

Developed an optimized matrix multiplication approach using 4x4 submatrices:

• SIMD Parallelization: Processed multiple elements simultaneously

Figure 5: 4x4 submatrix multiplication optimization

Performance Results

The optimization efforts resulted in:

• Significant reduction in processing time
• Improved throughput for real-time applications
• Better resource utilization

Figure 6: Performance results showing the improvement in processing time and throughput