Enterprise AI Analysis
A Survey On Neural Network Quantization
In recent years, with the extensive implementation of neural net- work models in fields such as computer vision and natural language processing, the issue of substantial model parameters and signifi- cant computational resource utilization has come to the fore. Neural network quantization techniques have emerged as a primary so- lution to address the constraints imposed by model deployment resources by reducing the numerical precision both of model pa- rameters (include weights, activation values and gradients). This paper undertakes a systematic exploration of quantization methods employed in traditional neural networks, including Convolutional Neural Networks and Recurrent Neural Networks, as well as neural networks based on the Transformer architecture. It delves into the technical distinctions between these methods and examines the challenges and limitations in their application. Additionally, it discusses the future directions and trends in this field.
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Traditional Neural Networks Overview
Quantization for traditional neural networks like CNNs and RNNs focuses on optimizing parameters to adapt to low-precision representations, balancing efficiency and accuracy. Early methods include dynamic parameter tuning and gradient correction (PACT, LSQ), evolving into techniques like Micro-Quantization modeling and probabilistic methods. Post-training quantization (PTQ) in this domain emphasizes theoretical foundations and data-independent schemes to enhance model performance under various constraints.
Large Language Models (LLMs) Overview
Quantization in LLMs primarily addresses the challenges of immense model sizes, high training costs, and inference latency. Techniques range from low-bit inference acceleration (Q-BERT) to mixed-precision layered processing (LLM.int8()) and dynamic quantization schemes (LLM-MQ). Current research focuses on ultra-low bit quantization with hardware adaptability, aiming for real-time, energy-efficient AI.
Vision Transformers Overview
Quantization for Vision Transformers (ViTs) focuses on adapting low-bit techniques to the unique self-attention and layer normalization mechanisms. Early work validated the feasibility of low-bit quantization, developing feature calibration and distribution-aware distillation. Recent advancements target non-uniform distribution issues, full quantization, and dynamic adaptation to improve robustness and maintain accuracy in ultra-low bit scenarios, including hardware-accelerated sparse quantization.
FP8
DeepSeekV3's Dynamic Hybrid Precision for Training
Enterprise Process Flow
High-precision Floating-point Parameters (FP32)
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Data Representation with Reduced Bits
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Minor Precision Loss (INT8)
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Substantial Computational Efficiency Enhancement
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Reduced Computational Resources
| Method of QAT | W/A(bit) | ResNet-18 | ResNet-50 | MobileNet-V2 |
|---|---|---|---|---|
| PACT | 4/4 | -1.2/-0.6 | -0.4/+0.1 | * |
| PACT | 2/2 | -6.0/* | -9.1/-5.2 | * |
| LSQ | 4/4 | +0.7/+0.3 | +0.7/+0.5 | * |
| LSQ | 1/2 | -2.6/-1.5 | -2.3/-1.3 | * |
| DAQ | 4/4 | * | * | -1.9/* |
Case Study: Llama3 Integration
With open-source models like Llama3 supporting FP8 fine-tuning, quantization has transitioned from a compression tool to a core driver of real-time, energy-efficient AI. This integration facilitates widespread adoption of low-precision models, making advanced AI more accessible and performant on diverse hardware, including edge devices.
| Method of PTQ | W/A(bit) | ViT-B | DeiT-S | DeiT-B | Swin-S | Swin-B |
|---|---|---|---|---|---|---|
| Full Prec. | 32/32 | 84.53 | 79.85 | 81.85 | 83.20 | 85.27 |
| PTQ4ViT | 8/8 | * | 77.47 | 80.48 | * | * |
| FQ-ViT | 8/8 | 83.31 | 71.61 | 81.20 | 82.71 | 82.97 |
| APQ-ViT | 8/8 | 84.26 | 79.78 | 81.72 | 83.16 | 86.40 |
| RepQ-ViT | 6/6 | 83.62 | 78.90 | 81.27 | 82.79 | 84.57 |
| AdaLog | 4/4 | 79.68 | 72.06 | 78.03 | 80.77 | 82.47 |
| DopQ-ViT | 6/6 | 84.02 | 79.30 | 81.96 | 82.95 | 84.97 |
| MPTQ-ViT | 6/6 | 83.12 | 79.29 | 81.29 | 63.08 | 60.18 |
| Mr.BiQ | 6/6 | 77.34 | 76.82 | 80.86 | * | * |
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