Self-Powering Dataflow Networks – Concepts and Implementation - Karim, Abrarul; Falk, Joachim; Schmidt, Dennis; Teich, Jürgen (Proceedings of the 22nd ACM-IEEE International Symposium on Formal Methods and Models for System Design (MEMOCODE), 2024)
Dataflow networks play a vital role in modeling and analyzing stream-processing systems in an analytic way, including digital signal and image processing systems. In this paper, we first present a system-level approach to synthesize such dataflow networks automatically to systems of communicating hardware actors connected by FIFO buffers. Although such data-triggered networks of (internally clocked) actors can achieve very high throughputs, the potential to power actors down in times of unavailability of data has not been addressed so far in any research. Here, we show that by refinement of the firing state machine of each actor in a given network, we enable the design of self-powering dataflow networks while exploiting clock gating as a means to save power in times of inactivity of each individual actor in a network. The gains of self-powering dataflow networks in terms of power and energy savings when powering down and up actors dynamically are shown for different data arrival patterns and rates in detailed experiments for multiple IoT system applications. These systems are often working in normally-off mode and wake up only upon the availability of data. For these, drastic energy savings are reporte