Exploring the Performance of Deep Neural Networks on Embedded Many-Core Processors

Abstract

This paper explores and evaluates the potential of deep neural network (DNN)-based machine learning algorithms on embed-ded many-core processors in cyber-physical systems, such as self-driving systems. To run applications in embedded systems, a plat-form characterized by low power consumption with high accuracy and real-time performance is required. Furthermore, a platform is required that allows the coexistence of DNN applications and other applications, including conventional real-time control soft-ware, to enable advanced embedded systems, such as self-driving systems. Clustered many-core processors, such as Kalray MPPA3-80 Coolidge, can run multiple applications on a single platform because each cluster can run applications independently. Moreover, MPPA3-80 integrates multiple arithmetic elements that operate at low frequencies, thereby enabling high performance and low power consumption comparable to that of embedded graphics processing units. Furthermore, the Kalray Neural Network (KaNN) code generator, a deep learning inference compiler for the MPPA3-80 platform, can efficiently perform DNN inference on MPPA3-80. This paper evaluates DNN models, including You Only Look Once (YOLO)-based and Single Shot MultiBox Detector (SSD)-based mod-els, on MPPA3-80. The evaluation examines the frame rate and power consumption in relation to the size of the input image, the computational accuracy, and the number of clusters.