Abstract 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.

中文翻译：

---

一种多核神经网络芯片上的无死锁物理映射方法

摘要 众核神经网络芯片被广泛应用于深度学习和神经形态计算应用。多核架构带来了高并行性，同时使模型到核的映射变得棘手。为了减少路由时间、传输数据包数量和能耗，以及核间数据移动的无死锁性能，我们制定了具有点对点和组播路径的路由策略下的物理映射优化问题. 将应用加权通信(WCA)定义为目标函数，并设计结合两个无死锁约束的模拟退火算法来解决映射问题。使用多层感知器 (MLP) 和卷积神经网络 (CNN) 应用程序进行评估。