AI Model Quantization Training for Semiconductor Chips
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI semiconductor solutions face performance degradation due to data precision loss when CNN model weights are quantized from floating-point to lower bit lengths for hardware implementation, and existing training methods do not account for hardware constraints, leading to suboptimal AI chip performance.
Innovation Solution
A training system that includes convolution quantization and activation quantization units to convert floating-point CNN model weights to fixed-point weights suitable for AI chips, along with a chip customization unit that optimizes resource utilization, mimicking hardware characteristics through techniques like batch normalization, image mean merge, and shift-based quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If CNN model weights are quantized from floating-point to lower bit lengths for hardware implementation, then device complexity is reduced and resource utilization is improved, but data precision is lost and AI chip performance degrades
Solution Approach 1:
The patent applies preliminary action by performing quantization-aware training before hardware deployment. The system simulates quantization effects during the training phase and adjusts weights to compensate for precision loss, ensuring optimal performance when the model is later deployed to quantized hardware. This pre-adjustment resolves the contradiction by preparing the model in advance for the precision constraints of hardware implementation.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting quantization parameters (bit lengths, precision levels) during training to find the optimal balance between hardware resource utilization and data precision. The system experiments with different quantization configurations and selects parameters that maintain acceptable precision while maximizing hardware efficiency, directly addressing the trade-off between these two features.
2Productivity
If training is performed in high-performance computing environment without accounting for hardware constraints, then training speed and productivity are improved, but AI chip performance degrades when model is loaded
Solution Approach 1:
The patent introduces an intermediary layer in the form of a simulation environment that bridges the high-performance computing training environment and the target AI chip hardware. This intermediary simulates hardware constraints and quantization effects during training, allowing the model to be optimized for the specific hardware platform without sacrificing training efficiency. The simulation acts as a mediator that translates hardware constraints into training objectives, resolving the contradiction between training productivity and deployment reliability.
Solution Approach 2:
The patent applies universality by creating a training framework that can operate in high-performance computing environments while simultaneously accounting for diverse hardware constraints. The system maintains compatibility with standard HPC training workflows while adding hardware-aware optimization capabilities, making the training process universally applicable to both development and deployment phases without compromising either training speed or chip performance.
3Measurement precision
If weights are stored in floating-point format, then data precision is maintained, but resource utilization and efficiency in AI chip decrease
Solution Approach 1:
The patent applies local quality by implementing differential quantization strategies for different parts of the neural network. Instead of uniformly quantizing all weights, the system identifies and preserves high-precision representation for critical weights that significantly impact model accuracy, while applying lower precision to less critical parameters. This localized approach to precision allocation maintains necessary data precision while maximizing resource utilization in the AI chip.
Data Source
AI summary
A system for training an artificial intelligence (AI) model for an AI chip to implement an AI task may include an AI training unit to train weights of an AI model in floating point, a convolution quantization unit for quantizing the trained weights to a number of quantization levels, and an activation quantization unit for updating the weights of the AI model so that output of the AI model based at least on the updated weights are within a range of activation layers of the AI chip. The updated weights may be stored in fixed point and uploadable to the AI chip. The various units may be configured to account for the hardware constraints in the AI chip to minimize performance degradation when the trained weights are uploaded to the AI chip and expedite training convergence. Forward propagation and backward propagation may be combined in training the AI model.