ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks (1811.01704v4)

Abstract: Deep Neural Networks (DNNs) typically require massive amount of computation resource in inference tasks for computer vision applications. Quantization can significantly reduce DNN computation and storage by decreasing the bitwidth of network encodings. Recent research affirms that carefully selecting the quantization levels for each layer can preserve the accuracy while pushing the bitwidth below eight bits. However, without arduous manual effort, this deep quantization can lead to significant accuracy loss, leaving it in a position of questionable utility. As such, deep quantization opens a large hyper-parameter space (bitwidth of the layers), the exploration of which is a major challenge. We propose a systematic approach to tackle this problem, by automating the process of discovering the quantization levels through an end-to-end deep reinforcement learning framework (ReLeQ). We adapt policy optimization methods to the problem of quantization, and focus on finding the best design decisions in choosing the state and action spaces, network architecture and training framework, as well as the tuning of various hyperparamters. We show how ReLeQ can balance speed and quality, and provide an asymmetric general solution for quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet, MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy (=< 0.3% loss) while minimizing the computation and storage cost. With these DNNs, ReLeQ enables conventional hardware to achieve 2.2x speedup over 8-bit execution. Similarly, a custom DNN accelerator achieves 2.0x speedup and energy reduction compared to 8-bit runs. These encouraging results mark ReLeQ as the initial step towards automating the deep quantization of neural networks.

ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

The research paper titled "ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks" introduces an automated framework, ReLeQ, designed to tailor quantization levels within deep neural networks (DNNs) using reinforcement learning. This paper addresses a significant computational challenge, namely, the reduction of resources required for DNN inference tasks, particularly in computer vision applications. It seeks to efficiently minimize the bitwidth of network encodings without sacrificing accuracy, a process conventionally constrained by manual tuning and high-dimensional hyper-parameter space issues.

Core Contributions

1. Reinforcement Learning Framework: The paper presents a novel reinforcement learning (RL) framework that automates the selection of quantization levels for individual layers within neural networks. Leveraging the Proximal Policy Optimization (PPO) algorithm, ReLeQ is strategically crafted to optimize and learn bitwidth assignments that preserve the DNN's classification accuracy.
2. State and Action Space Design: The design of ReLeQ includes both static parameters (such as layer identity and initial weight distribution) and dynamic parameters (including the current state of accuracy and quantization) to navigate the quantization landscape effectively. By structuring the quantization problem through a multi-objective optimization lens, ReLeQ employs a quantization action space which allows flexible bitwidth selection, accommodating network-specific sensitivity.
3. Asymmetric Reward Function: The reward formulation within ReLeQ is asymmetrically inclined towards maintaining accuracy, thus ensuring that the benefits of reduced computation and storage do not compromise the model's predictive performance. Through reward shaping techniques, the system effectively balances multiple objectives.
4. Broad Applicability: ReLeQ demonstrates its utility across various network architectures, including AlexNet, MobileNet, VGG-11, ResNet-20, and others. It delivers a generalized solution capable of achieving heterogeneous bitwidth quantization levels across networks while ensuring minimal accuracy discrepancies (≤0.3% loss).

Performance Insights

Through rigorous evaluation, ReLeQ achieves quantization results that altogether yield a speedup of 2.2x using conventional processors and 2.0x with custom DNN accelerators, surpassing 8-bit standard execution. Moreover, it concurrently delivers substantial energy savings, as evidenced by its deployment on custom accelerators like Stripes.

The RL-driven solution also underscores a critical advancement over other heuristic-based methods, such as ADMM, by exhibiting improved performance gains both in speed and energy metrics, notably when applied to benchmark models like AlexNet and LeNet.

Theoretical and Practical Implications

Theoretically, ReLeQ illustrates a substantive shift towards autonomous optimization of neural networks by employing a data-driven, RL-based exploration beyond traditional manual interventions. This capacity to effectively traverse the hyper-parameter space opens new avenues for energy-efficient AI deployment, espousing broader adaptation within edge devices and resource-limited environments.

Practically, the ReLeQ framework suggests a paradigm where model efficiency is achievable without expert oversight, encouraging a scalable and hardware-agnostic methodology for modern neural network operations. The potential extension of this approach could involve adaptive quantization granularity, per-channel adjustments, and myriad personalization factors tailored by diverse target hardware needs.

Future Directions

Looking ahead, the incorporation of reinforcement learning in neural network quantization emphasizes the evolving landscape of AI where systems autonomously learn and optimize vital parameters. Further research could explore deeper integration with neural architecture search techniques to enhance model architecture and parameter simultaneity. Additionally, as quantization methods mature, cross-operability with advanced neural network compression strategies (i.e., pruning, clustering) may yield further efficiency dividends.

Overall, the ReLeQ model presents an efficacious stride toward automated quantization, ensuring that neural networks remain computationally viable while preserving their haLLMark accuracy, a critical consideration for sustainable AI advancement.