Software-defined Significance-Driven Computing

We introduce task-significance, a novel concept that describes the <br>contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific<br>knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user<br>constraints.<br><br>Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a<br>user-space interface for memory allocation from the unreliable portion of the physical memory.

Having this framework in place<br>allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM).<br>We use this property to implement techniques for task-based applications that minimise the probability of errors when using<br>unreliable memory enabling increased quality and power efficiency when using unreliable DRAM.