LAUSR

The era of mobile communications and the Internet of Things (IoT) has introduced numerous challenges for mobile processing platforms that are responsible for increasingly complex signal processing tasks from different application domains. In recent years, the power efficiency of computing has been improved by adding more parallelism and workload-specific computing resources to such platforms. However, programming of parallel systems can be time-consuming and challenging if only low-level programming methods are used. This work presents a dataflow-based co-design framework TTADF that reduces the design effort of both software and hardware design for mobile processing platforms. The paper presents three application examples from the fields of video coding, machine vision, and wireless communications. The application examples are mapped and profiled both on a pipelined and a shared-memory multicore platform that is generated by TTADF. The results of the TTADF co-design-based solutions are compared against previous manually created designs and a recent dataflow-based design flow, showing that TTADF provides very high energy efficiency together with a high level of automation in software and hardware design.