MultiObjective GPU design space exploration optimization

Jooya, Ali; Dimopoulos, N.J.; Baniasadi, Amirali

doi:10.1109/hpcsim.2016.7568398

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…Applying ML for system design has a twofold meaning: 1 the reduction of burdens on human experts designing systems manually, and 2 the close of the positive feedback loop, i.e., architecture/system for ML and simultaneously ML for architecture/system, encouraging improvements on both sides. These applications include predictive performance modeling [18,35,44,45,52,56,90], efficient design space exploration [36,38,49,92], cache replacement [5,70,80], prefetcher [8,28,93], branch prediction [25,37], NoC design [21,63,85], power and resource management [4,31], task allocation [51,94], malware detection [15,59], compiler design [53,76], and so on.…”

Section: Related Workmentioning

confidence: 99%

Core Placement Optimization for Multi-chip Many-core Neural Network Systems with Reinforcement Learning

Deng

et al. 2020

ACM Trans. Des. Autom. Electron. Syst.

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Multi-chip many-core neural network systems are capable of providing high parallelism benefited from decentralized execution, and they can be scaled to very large systems with reasonable fabrication costs. As multi-chip many-core systems scale up, communication latency related effects will take a more important portion in the system performance. While previous work mainly focuses on the core placement within a single chip, there are two principal issues still unresolved: the communication-related problems caused by the non-uniform, hierarchical on/off-chip communication capability in multi-chip systems, and the scalability of these heuristic-based approaches in a factorially growing search space. To this end, we propose a reinforcement-learning-based method to automatically optimize core placement through deep deterministic policy gradient, taking into account information of the environment by performing a series of trials (i.e., placements) and using convolutional neural networks to extract spatial features of different placements. Experimental results indicate that compared with a naive sequential placement, the proposed method achieves 1.99× increase in throughput and 50.5% reduction in latency; compared with the simulated annealing, an effective technique to approximate the global optima in an extremely large search space, our method improves the throughput by 1.22× and reduces the latency by 18.6%. We further demonstrate that our proposed method is capable to find optimal placements taking advantages of different communication properties caused by different system configurations, and work in a topology-agnostic manner. CCS Concepts: • Computer systems organization → Architectures; Parallel architectures; • Computing methodologies → Reinforcement learning;

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