Deep Q Learning Hardware Architecture for Stable Training
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Solution Overview
Problem
Current processing architectures are not optimized to efficiently implement the deep Q learning technique, which utilizes separate target and prediction networks and random experience replay to train artificial neural networks for decision-making tasks, leading to instability and inefficiencies in weight updates.
Innovation Solution
A hardware architecture is designed with inference cores for tuple generation, training cores for weight updates, a replay memory for storing experience tuples, and a high-speed copy engine to periodically update the target network weights, optimizing the deep Q learning process by separating the roles of prediction and target networks and enabling efficient weight stabilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current processing architectures are used to implement deep Q learning, then the implementation can be performed on general-purpose hardware, but the training process becomes unstable and inefficient due to lack of optimization
Solution Approach 1:
The architecture is segmented into distinct functional units: inference cores for tuple generation, training cores for weight updates, replay memory for experience storage, and copy engine for weight synchronization. This segmentation allows each component to be optimized for its specific function while maintaining overall system stability and efficiency.
2Stability of the object's composition
If separate target and prediction networks are used in deep Q learning, then the model can maintain stability during training, but the device complexity increases
Solution Approach 1:
The architecture merges the target network and prediction network into a unified system where both networks share the same underlying structure and are coordinated through the copy engine. This reduces redundancy while maintaining the stability benefits of separate networks during training.
3Reliability
If random experience replay is implemented, then temporally correlated samples are avoided, but the memory requirements and data management complexity increase
Solution Approach 1:
The replay memory acts as an intermediary between the inference process and training process, storing experience tuples and enabling random sampling. This mediator component manages the complexity of data storage and retrieval, allowing the rest of the system to focus on its primary functions while benefiting from decorrelated training samples.
4Stability of the object's composition
If high-speed copy engine is used to periodically update target network weights, then weight stabilization is improved, but the energy consumption and operational complexity increase
Solution Approach 1:
The copy engine performs weight updates periodically rather than continuously, synchronizing target network weights with prediction network weights at fixed intervals. This periodic action maintains weight stability while significantly reducing energy consumption compared to continuous copying operations.
Data Source
AI summary
The deep Q learning technique trains weights of an artificial neural network using a number of unique features, including separate target and prediction networks, random experience replay to avoid issues with temporally correlated training samples, and others. A hardware architecture is described that is tuned to perform deep Q learning. Inference cores use a prediction network to determine an action to apply to an environment. A replay memory stores the results of the action. Training cores use a loss function derived from outputs from both the target and prediction networks to update weights of the prediction neural networks. A high speed copy engine periodically copies weights from the prediction neural network to the target neural network.