Choices of dataflows, which are known as intra-core neural network (NN) computation loop nest scheduling and inter-core hardware mapping strategies, play a critical role in the performance and energy efficiency of NoC-based neural network accelerators. Confronted with an enormous dataflow exploration space, this paper proposes an automatic framework for generating and optimizing the full-layer-mappings based on two reinforcement learning algorithms including A2C and PPO. Combining soft and hard constraints, this work transforms the mapping configuration into a sequential decision problem and aims to explore the performance and energy efficient hardware mapping for NoC systems. We evaluate the performance of the proposed framework on 10 experimental neural networks. The results show that compared with the direct-X mapping, the direct-Y mapping, GA-base mapping, and NN-aware mapping, our optimization framework reduces the average execution time of 10 experimental NNs by 9.09$\%$, improves the throughput by 11.27$\%$, reduces the energy by 12.62$\%$, and reduces the time-energy-product (TEP) by 14.49$\%$. The results also show that the performance enhancement is related to the coefficient of variation of the neural network to be computed.

This correspondence presents a segmentation and fitting method using a new robust estimation technique. We present a robust estimation method with high breakdown point which can tolerate more than 80% of outliers. The method randomly samples appropriate range image points in the current processing region and solves equations determined by these points for parameters of selected primitive type. From K samples, we choose one set of sample points that determines a best-fit equation for the largest homogeneous surface patch in the region. This choice is made by measuring a residual consensus (RESC), using a compressed histogram method which is effective at various noise levels. After we get the best-fit surface parameters, the surface patch can be segmented from the region and the process is repeated until no pixel left. The method segments the range image into planar and quadratic surfaces. The RESC method is a substantial improvement over the least median squares method by using histogram approach to inferring residual consensus. A genetic algorithm is also incorporated to accelerate the random search.>

The authors randomly sample appropriate range image points and solve equations determined by these points for the parameters of selected primitive type. From K samples they measure residual consensus to choose one set of sample points that determines an equation having the best fit for the largest homogeneous surface patch in the current processing region. The residual consensus is measured by a compressed histogram method that works at various noise levels. The estimated surface patch is extracted out of the processing region to avoid further computation. A genetic algorithm is used to accelerate the search speed.>