Abstract
Network-on-Chip (NoC) is a scalable on-chip communication architecture for the NN accelerator, but with the increase in the number of nodes, the communication delay becomes higher. Applications such as machine learning have a certain resilience to noisy/erroneous transmitted data. Therefore, approximate communication becomes a promising solution to improving performance by reducing traffic loads under the constraint of the acceptable maximum accuracy loss of neural networks. It is a key issue to balance the result quality and the communication delay for approximate NoC systems. The traditional approximate NoC only considers the node-to-node approximation-based dynamic traffic regulation. However, the dynamically changing traffic patterns across different nodes, different times, and different applications lead to a huge search space, which makes it hard to explore an optimal global approximation solution. In this paper, we propose a quality model for different neural networks, which presents the relationship between the quality loss and the data approximate rate. Then, a hierarchical approximate scheme optimized with reinforcement learning (HAS-RL) is proposed and we reduce the complexity of the HAS-RL by reducing the state space and action space, which will reduce the resource overhead as well. After that, we embed a global approximate controller in the NoC system, in which we deploy a policy network trained with the offline reinforcement learning algorithm to adjust the data approximate rates of each node at run time. Compared with the state-of-the-art method, the proposed scheme reduces the average network delay by $13.5\%$ while their accuracies are similar. The proposed HAS-RL only causes an additional area overhead of $1.24\%$ and power consumption of $0.77\%$ compared with the traditional router design.
Original language | English |
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Pages (from-to) | 1863-1875 |
Number of pages | 13 |
Journal | IEEE Transactions on Circuits and Systems I: Regular Papers |
Volume | 71 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Offline reinforcement learning
- neural network
- approximate communication
- network-on-chip