Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

Core Concepts

This work introduces mixed-precision neural networks that employ quantized low-precision fixed-point parameters to reduce the computational complexity and memory footprint of digital predistortion (DPD) for wideband power amplifiers, thereby lowering power consumption without compromising linearization efficacy.

Abstract

This article presents a novel approach to implementing mixed-precision (MP) arithmetic operations and model parameters in a gated Recurrent Neural Network (RNN)-based Digital Pre-distortion (DPD) model for wideband power amplifiers (PAs). The proposed method aims to curtail the DPD model inference power consumption by substituting most high-precision floating-point operations with low-precision fixed-point operations through quantizing neural network weights (W) and activations (A).
The key highlights and insights are:

The substantial computational complexity and memory requirements of machine learning-based DPD systems, especially those using deep neural networks (DNNs), pose significant obstacles to their efficient deployment in wideband transmitters, particularly in the context of future 5.5G/6G base stations or Wi-Fi 7 routers, where limited power resources constrain real-time DPD model computation.

The authors adopt a mixed-precision strategy utilizing low-precision fixed-point integer arithmetic for inference to enhance the energy efficiency of DPD models. This method involves a quantization scheme that converts the model's weights and activations, including other intermediate variables, to lower precision while retaining full-precision operations for feature extraction from the I/Q signal.

Experimental results show that the proposed W16A16-GRU DPD model achieves no performance loss against 32-bit floating-point precision DPDs, while attaining -43.75 (L)/-45.27 (R) dBc in Adjacent Channel Power Ratio (ACPR) and -38.72 dB in Error Vector Magnitude (EVM). A 16-bit fixed-point-precision MP-DPD enables a 2.8× reduction in estimated inference power consumption compared to the full-precision baseline.

The authors also demonstrate that lower-precision configurations, such as W8A8 and W12A12, can achieve up to 4.5× and 3.8× power reduction, respectively, at the expense of some linearization performance.

The proposed MP-DPD approach is compatible with existing strategies, allowing for further power savings when combined, making it a promising solution for energy-efficient wideband transmitters in power-sensitive environments.

Stats

The DPD input I/Q data sample rate is 640 MHz.
The PAPR of the test signal is 10.38 dB.
The DPA outputs at 13.75 dBm.

Quotes

"As bandwidths in future radio systems expand, the energy demands of DPD computation intensify."
"Utilizing 32-bit floating-point (FP32) arithmetic, while beneficial for accuracy, can increase model size, negatively impacting energy efficiency."
"The energy consumption of on-chip Static Random Access Memory (SRAM) is up to 12.2× higher than that of a MAC operation. Moreover, the energy costs for off-chip memory access are roughly three orders of magnitude greater than for arithmetic operations."

Key Insights Distilled From

MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers

by Yizhuo Wu,An... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15364.pdf

Deeper Inquiries

How can the proposed MP-DPD approach be further optimized to achieve even greater power savings without significantly compromising linearization performance

To further optimize the proposed MP-DPD approach for greater power savings without compromising linearization performance, several strategies can be implemented:

Dynamic Precision Adjustment: Implement a dynamic precision adjustment mechanism that adapts the precision of weights and activations based on the complexity of the input signal. By dynamically adjusting precision levels, the model can maintain high accuracy during critical signal processing while reducing precision during less complex operations, leading to overall power savings.

Sparsity Exploitation: Incorporate sparsity techniques to reduce the number of computations required in the neural network. By identifying and eliminating unnecessary computations through sparsity patterns, the model can achieve significant power savings without sacrificing performance.

Quantization-Aware Training: Enhance the quantization-aware training process to optimize the quantization scales more efficiently. By fine-tuning the quantization scales during training, the model can achieve better precision control and energy efficiency, leading to improved power savings.

Model Compression Techniques: Implement model compression techniques such as pruning, weight sharing, or knowledge distillation to reduce the overall model size. By compressing the model while maintaining performance, the power consumption can be significantly reduced, especially during inference.

Hardware Acceleration: Utilize specialized hardware accelerators designed for low-precision computations. By offloading computations to hardware optimized for mixed-precision operations, the model can achieve higher efficiency and power savings.

What are the potential challenges and trade-offs in deploying the MP-DPD model in real-world 5.5G/6G base stations or Wi-Fi 7 routers

Deploying the MP-DPD model in real-world 5.5G/6G base stations or Wi-Fi 7 routers may present several challenges and trade-offs:

Hardware Compatibility: Ensuring compatibility with existing hardware platforms in base stations or routers may require modifications or upgrades to support the mixed-precision computations efficiently. Compatibility issues could arise if the hardware is not optimized for low-precision operations.

Latency Concerns: Implementing MP-DPD may introduce additional latency due to the quantization and dynamic precision adjustments. Balancing the trade-off between latency and power savings is crucial, especially in real-time communication systems.

Model Complexity: Managing the complexity of the MP-DPD model and integrating it into the existing signal processing chain without disrupting the overall system operation is a significant challenge. Ensuring seamless integration and interoperability is essential.

Regulatory Compliance: Meeting regulatory standards and ensuring that the MP-DPD model does not introduce any interference or non-compliance issues in wireless communication systems is critical. Compliance with industry standards and regulations is necessary for deployment.

Resource Constraints: Base stations and routers may have limited resources in terms of power, memory, and processing capabilities. Optimizing the MP-DPD model to operate efficiently within these constraints while maintaining performance is a key challenge.

Given the insights on the energy costs of memory access versus arithmetic operations, how could novel hardware architectures be designed to better support energy-efficient DPD computation

Novel hardware architectures can be designed to better support energy-efficient DPD computation by addressing the energy costs of memory access versus arithmetic operations:

Memory Hierarchy Optimization: Designing a memory hierarchy that minimizes the energy consumption of memory access operations can significantly improve efficiency. Utilizing on-chip memory with lower energy costs and optimizing data movement between different memory levels can reduce overall power consumption.

In-Memory Computing: Exploring in-memory computing techniques where computation is performed within memory units can reduce the energy overhead associated with data movement. By integrating computation and memory functions, the need for frequent data transfers can be minimized, leading to energy savings.

Customized Processing Units: Developing customized processing units tailored for DPD computations can optimize energy efficiency. Specialized hardware accelerators designed specifically for DPD tasks can reduce power consumption by efficiently executing the required operations with minimal energy overhead.

Parallel Processing: Implementing parallel processing architectures that distribute computations across multiple processing units can improve energy efficiency. By parallelizing tasks and optimizing workload distribution, the overall power consumption can be reduced while maintaining performance.

Low-Power Design Techniques: Incorporating low-power design techniques such as clock gating, voltage scaling, and power gating at the hardware level can further enhance energy efficiency. By implementing power-saving mechanisms at the architectural level, the hardware can operate more efficiently during DPD computations.