Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network applications.Meanwhile,with neural network architectures going deeper and wider,the limited memory cap...Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network applications.Meanwhile,with neural network architectures going deeper and wider,the limited memory capacity has become a constraint to deploy models on UNNA platforms.Therefore how to efficiently manage memory space and how to reduce workload footprints are urgently significant.In this paper,we propose Tetris:a heuristic static memory management framework for UNNA platforms.Tetris reconstructs execution flows and synchronization relationships among cores to analyze each tensor’s liveness interval.Then the memory management problem is converted to a sequence permutation problem.Tetris uses a genetic algorithm to explore the permutation space to optimize the memory management strategy and reduce memory footprints.We evaluate several typical neural networks and the experimental results demonstrate that Tetris outperforms the state-of-the-art memory allocation methods,and achieves an average memory reduction ratio of 91.9%and 87.9%for a quad-core and a 16-core Cambricon-X platform,respectively.展开更多
基金the Beijing Natural Science Foundation under Grant No.JQ18013the National Natural Science Foundation of China under Grant Nos.61925208,61732007,61732002 and 61906179+1 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(CAS)under Grant No.XDB32050200the Youth Innovation Promotion Association CAS,Beijing Academy of Artificial Intelligence(BAAI)and Xplore Prize.
文摘Uniform memory multicore neural network accelerators(UNNAs)furnish huge computing power to emerging neural network applications.Meanwhile,with neural network architectures going deeper and wider,the limited memory capacity has become a constraint to deploy models on UNNA platforms.Therefore how to efficiently manage memory space and how to reduce workload footprints are urgently significant.In this paper,we propose Tetris:a heuristic static memory management framework for UNNA platforms.Tetris reconstructs execution flows and synchronization relationships among cores to analyze each tensor’s liveness interval.Then the memory management problem is converted to a sequence permutation problem.Tetris uses a genetic algorithm to explore the permutation space to optimize the memory management strategy and reduce memory footprints.We evaluate several typical neural networks and the experimental results demonstrate that Tetris outperforms the state-of-the-art memory allocation methods,and achieves an average memory reduction ratio of 91.9%and 87.9%for a quad-core and a 16-core Cambricon-X platform,respectively.
