Multi-Agent RL Weight Pruning via Sparsity Parallel Processing
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Solution Overview
Problem
Multi-agent reinforcement learning systems face challenges in power consumption and learning stability due to iterative operations with shared network weights, and existing pruning schemes are not adequately tested for deep reinforcement learning, leading to uncertainty about the impact of weight removal on accuracy.
Innovation Solution
A system is proposed that includes an on-chip encoding unit, sparse weight workload allocation, and sparsity parallel processing architecture using vector processing, which generates sparse data through weight grouping and compression, allowing for efficient weight pruning while maintaining accuracy, and is implemented on an FPGA for high throughput and power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If weight pruning is applied to reduce network size and memory usage, then power consumption and memory space are reduced, but accuracy may deteriorate due to removal of important weights
Solution Approach 1:
The patent applies preliminary action by performing weight pruning during the training process rather than after training completes. The pruning operation is integrated into the training loop, allowing the network to learn with a sparser structure from the beginning, thus preventing the network from relying on pruned weights and maintaining accuracy while reducing power consumption.
Solution Approach 2:
The patent implements feedback mechanisms where the pruning decisions are continuously adjusted based on training performance. The system monitors accuracy metrics and adjusts which weights are pruned accordingly, ensuring that only weights that do not significantly impact accuracy are removed, thus maintaining learning accuracy while achieving energy savings.
2Stability of the object's composition
If iterative operations with shared network weights are used for learning stability, then learning stability is improved, but power consumption increases due to repeated computations
Solution Approach 1:
The patent extracts and removes redundant computational operations by identifying and eliminating weights that contribute minimally to the learning outcome. By taking out these unnecessary weight computations from the iterative process, the system maintains learning stability through the remaining important weights while significantly reducing the power consumption associated with repeated computations.
Solution Approach 2:
The patent applies discarding and recovering by temporarily setting certain weights to zero (discarding) during specific training iterations, then recovering them when needed. This allows the system to reduce computational load and power consumption during stable learning phases while recovering full computational capability when stability needs are lower, thus balancing learning stability and energy usage.
3Quantity of substance
If existing pruning schemes are applied to deep reinforcement learning, then network size is reduced, but accuracy cannot be guaranteed due to lack of validation in long term decision problems
Solution Approach 1:
The patent applies dynamics by making the pruning structure adaptive and changeable during training rather than static. The pruning mask is dynamically adjusted based on training progress and performance feedback, allowing the system to reduce network size progressively while maintaining accuracy guarantees for long-term decision problems through continuous adaptation to the specific reinforcement learning task.
Data Source
AI summary
The present disclosure provides a system for accelerating multi-agent reinforcement learning through sparsity processing and an operating method thereof and proposes an acceleration system, which can analyze a weight pruning algorithm capable of guaranteeing accuracy suitably for characteristics of multi-agent reinforcement learning and includes an on-chip encoding unit, a sparse weight workload allocation unit, and sparsity parallel processing architecture through vector processing, which can effectively support the weight pruning algorithm, and an operating method of the system. Furthermore, the present disclosure proposes an acceleration platform that constitutes a circuit in a way to be suitable for a deep learning model from its initial step while having high throughput and power efficiency by using an FPGA, not a GPU in which several thousands of cores have been integrated and which generate many and consume great power.