Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization

Core Concepts

EQO, a quantized 2PC inference framework, jointly optimizes convolutional neural networks and 2PC protocols to achieve ultra-efficient private inference by combining Winograd transformation and mixed-precision quantization.

Abstract

The paper proposes EQO, a quantized 2PC inference framework that jointly optimizes convolutional neural networks (CNNs) and 2PC protocols to achieve ultra-efficient private inference. EQO features the following key innovations:

Novel 2PC protocol: EQO combines Winograd transformation with quantization for efficient convolution computation in 2PC inference. However, the authors observe that naively combining quantization and Winograd convolution is sub-optimal, as Winograd transformations introduce extensive local additions and weight outliers that increase the quantization bit widths and require frequent bit width conversions with non-negligible communication overhead.

Protocol-level optimizations: To address the challenges introduced by Winograd transformations, EQO proposes a series of optimizations for the 2PC inference graph to minimize the communication.

Network-level optimizations: EQO develops a sensitivity-based mixed-precision quantization algorithm to optimize network accuracy given communication constraints. It also proposes a 2PC-friendly bit re-weighting algorithm to accommodate weight outliers without increasing bit widths.

Through extensive experiments, EQO demonstrates significant communication reduction (11.7×, 3.6×, and 6.3×) compared to state-of-the-art frameworks SiRNN, COINN, and CoPriv, while achieving higher accuracy (1.29%, 1.16%, and 1.29% respectively).

Stats

The total communication of OT-based 2PC inference scales with both the bit widths and the number of multiplications in linear layers.
Naively combining Winograd transformation and quantization only reduces ~20% communication compared to not using Winograd convolution.
Winograd transformations introduce more weight outliers that make low-precision quantization challenging.

Quotes

"EQO features a novel 2PC protocol that combines Winograd transformation with quantization for efficient convolution computation."
"We observe naively combining quantization and Winograd convolution is sub-optimal: Winograd transformations introduce extensive local additions and weight outliers that increase the quantization bit widths and require frequent bit width conversions with non-negligible communication overhead."
"EQO demonstrates 11.7×, 3.6×, and 6.3× communication reduction with 1.29%, 1.16%, and 1.29% higher accuracy compared to state-of-the-art frameworks SiRNN, COINN, and CoPriv, respectively."

Key Insights Distilled From

EQO: Exploring Ultra-Efficient Private Inference with Winograd-Based Protocol and Quantization Co-Optimization

by Wenxuan Zeng... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09404.pdf

Deeper Inquiries

How can the proposed bit re-weighting algorithm be generalized to handle outliers introduced by other network optimizations beyond Winograd transformations

The proposed bit re-weighting algorithm in EQO can be generalized to handle outliers introduced by other network optimizations beyond Winograd transformations by following a similar approach. The key idea is to adjust the representation range of the weights based on their distribution characteristics. Here's how this generalization can be achieved:

Identifying Outliers: The first step is to analyze the weight distribution after applying the specific network optimization. This analysis helps in identifying outliers that deviate significantly from the majority of weights.

Range Adjustment: Similar to the bit re-weighting algorithm in EQO, the range of the weights can be adjusted to accommodate outliers without increasing the overall bit width. This adjustment ensures that the quantization process can capture the full range of weights effectively.

Sensitivity Analysis: Conduct sensitivity analysis to determine the impact of outliers on the overall network performance. By understanding how outliers affect the network accuracy, it becomes easier to devise a strategy to handle them without compromising the model's performance.

Adaptive Quantization: Implement adaptive quantization techniques that dynamically adjust the bit widths based on the distribution of weights. This adaptive approach ensures that outliers are appropriately represented without unnecessarily increasing the bit widths for all weights.

Iterative Refinement: Iterate on the bit re-weighting algorithm based on the specific characteristics of the outliers introduced by different network optimizations. Fine-tuning the algorithm to suit the particular optimization techniques used in the network can enhance its effectiveness in handling outliers.

By following these steps and customizing the bit re-weighting algorithm to suit the characteristics of outliers introduced by various network optimizations, EQO's approach can be extended to handle outliers effectively in a generalized manner.

What are the potential trade-offs between the communication reduction and the computational overhead introduced by the Winograd-based protocol and the quantization algorithms in EQO

The potential trade-offs between communication reduction and computational overhead introduced by the Winograd-based protocol and the quantization algorithms in EQO are crucial considerations in optimizing private inference efficiency. Here are the key trade-offs to be mindful of:

Communication vs. Computation: The Winograd-based protocol aims to reduce communication overhead by optimizing the convolution computation. However, this optimization may introduce additional computational complexity due to the transformation process. Balancing the reduction in communication with the computational overhead is essential to ensure overall efficiency.

Quantization Precision: Quantization algorithms aim to reduce the number of bits required to represent weights and activations, thereby decreasing communication costs. However, lower bit precision may lead to accuracy degradation, necessitating a trade-off between communication reduction and model performance.

Protocol Fusion: The fusion of protocols in EQO may streamline communication but could introduce computational overhead in managing the combined operations. Evaluating the impact of protocol fusion on both communication and computation is vital for optimizing overall efficiency.

Bit Re-weighting Impact: The bit re-weighting algorithm addresses outliers to enhance quantization efficiency. However, the computational complexity of adjusting weights to handle outliers should be weighed against the communication benefits gained from improved quantization.

Scalability Considerations: As the network scales, the trade-offs between communication reduction and computational overhead may vary. Ensuring that the optimizations in EQO are scalable and maintain a balance between communication efficiency and computational cost is essential.

By carefully analyzing and optimizing these trade-offs, EQO can achieve a well-balanced approach to ultra-efficient private inference.

Can the joint optimization framework of EQO be extended to other types of neural network layers beyond convolution, and how would the optimization strategies differ

The joint optimization framework of EQO can be extended to other types of neural network layers beyond convolution by adapting the optimization strategies to suit the characteristics of different layers. Here's how the optimization strategies may differ for other types of layers:

Fully Connected Layers: For fully connected layers, the optimization focus may shift towards reducing the communication and computation overhead associated with matrix multiplications. Strategies like quantization, protocol fusion, and sensitivity-based optimization can be applied to enhance efficiency.

Recurrent Layers: In recurrent neural networks (RNNs), optimizations may target reducing the communication overhead during sequential computations. Techniques like protocol optimization for recurrent operations and adaptive quantization for recurrent weights can be employed.

Pooling and Activation Layers: Optimization for pooling and activation layers may involve minimizing the communication cost of transmitting intermediate results. Strategies such as quantization-aware training and protocol fusion specific to these layers can be beneficial.

Skip Connections and Residual Blocks: For architectures with skip connections or residual blocks, the optimization framework may focus on handling the additional complexity introduced by these connections. Adaptive quantization and protocol adjustments to accommodate the unique structure of these layers can be key optimization strategies.

Attention Mechanisms: Networks with attention mechanisms may require specialized optimization techniques to manage the communication and computation demands of attention operations. Tailored approaches for quantization, protocol fusion, and sensitivity analysis specific to attention layers can enhance overall efficiency.

By extending the joint optimization framework of EQO to different types of neural network layers and customizing the optimization strategies to suit their specific characteristics, ultra-efficient private inference can be achieved across a variety of network architectures.