A deadlock-free physical mapping method on the many-core neural network chip

摘要

Many-core neural network chip is widely developed and used for both the deep learning and neuromorphic computing applications. Many-core architecture brings high parallelism while makes the model-to-core mapping intractable. In order to decrease the routing time, transmission packets amount and energy consumption, along with deadlock-free performance for inter-core data movement, we formulate an optimization problem for the physical mapping under the routing strategies with point-to-point and multicast paths. The Weighted Communication of Application(WCA) is defined as the objective function and simulated annealing algorithm incorporated with two deadlock-free constraints is designed to solve the mapping problem. Multi-layer perceptron(MLP) and convolutional neural network(CNN) applications are used for evaluation. Experimental results show that the proposed algorithm is quite efficient saving the routing time and power comsumption for inter-core communication, and the routing diversity has been significantly improved, the hotspot paths are greatly reduced after optimization, compare with the baseline of zigzag and neighbor mapping. (C) 2020 Elsevier B.V. All rights reserved.