Heterogeneous computing platforms for resource-aware video and data analytics (HOPWARE)

点击翻译按钮获取中文摘要

HOPWARE will investigate how the multi-objective platform created in the CERBERO H2020 project (https://www.cerbero-h2020.eu/) can be applied to heterogeneous computing platforms that combine deep-learning compute units with different capabilities, performance and power characteristics. The proposed heterogeneous computing platform in HOPWARE combines devices like Google EdgeTPUs, Intel VPUs, ARM processors and Xilinx FPGAs programmable via a single machine-learning framework like TensorFlow so that once training completes the network can be run in an optimal configuration of multiple devices. Project partner UPM created in CERBERO an adaptive framework called ARTICO that can assess the status of the system at run-time and deploy different reconfiguration strategies to improve its energy efficiency, robustness and performance. In this research we are particularly interested in the ARTICO/CERBERO approach to run-time monitoring, sensing and estimation of capabilities to assist in the decision of how to distribute the available resources to achieve a functional goal within constraints in power and execution time.