Reducing memory requirements through task recomputation in embedded multi-CPU systems

As embedded applications are processing increasingly larger data sets, keeping their memory space consumptions under control is becoming a very pressing issue. Observing this, several prior efforts have considered memory space reduction techniques (in both hardware and software) based on data compression and lifetime-based memory recycling (garbage collection). In this work, we propose and evaluate an alternate approach to memory space saving in multi-CPU embedded systems such as chip multiprocessors. The unique characteristic of our approach is that it recomputed the results of select tasks in a given task graph (which represents the application), instead of storing these results in memory and accessing them from there as needed.