| Wearable and embedded wireless sensor platforms are often highly constrained in size and power consumption in order to operate in unintrusive ways. To meet these constraints, the memory is kept as small as possible to reduce area and power, but at the same time it poses new challenges on programming. To address these problems, this dissertation proposes (1) a framework for synthesis of host-assisted scripting engines, (2) a minimum-overhead yet powerful runtime system based on recursive threaded code, and (3) a host-assisted memory optimization technique that exploits high-level knowledge in models of computation. The benefits are three-fold: first, the interactive access between programmer and embedded system improves productivity of programmers. Second, the low runtime overhead and memory requirements enable this runtime system to run on compact platforms while providing high flexibility and composability. Third, a new memory optimization scheme combined with the dispatching structure exposes regularity of dataflow models to enable memory and scheduling optimizations, sometimes in ways better than manual coding.;Experimental results show that systems developed using these techniques have unleashed the power of these resource-constrained networked embedded systems. As memory is currently the limiting factor, the lightweight runtime system and systematic memory optimization technique presented in this dissertation are enabling technology for a new class of real-world, ultra-compact networked embedded systems. |
