Low-power architecture with scratch-pad memory for accelerating embedded applications with run-time reuse

Current embedded systems are usually designed for data-dominated applications, but they have a tight energy and time budget. Scratch-pad memories are completely software-controlled memories with predictable behaviour and good performance and energy characteristics, thus they tend to become a standard feature in many embedded systems. However, their predictability is not helping if the application accesses its data dynamically, when the addresses of the accessed data depend on the application´s input. In such cases, predetermining the scratch-pad content at design-time is not always possible as the compiler cannot predict the runtime input. Moreover, in this case, both data reuse and data placement in the scratch-pad are inefficient because chunks of data already stored cannot be efficiently reused and combined with the runtime accessed data blocks. State-of-the art techniques copy each new data block to the scratch-pad without considering whether portions of them are already in it. Such dynamic temporal locality cannot be predicted or exploited by the compiler. The authors here present a system architecture, strongly connected to the system´s scratch-pad and the processor´s compiler, which is able to efficiently exploit run-time data reuse in the scratch-pad by being capable of holding valuable information, such as the exact data contents of the scratch-pad at runtime, and using it to do all the necessary operations for placing each new data block in scratch-pad. It is fine tuned for applications with run-time reuse between rectangular data blocks. The application domain of the proposed architecture is multimedia applications with run-time reuse, certain applications with linked lists and multi-threaded applications. It operates in a time and energy-efficient manner when compared with existing scratch-pad architectures without the authors´ scratch-pad accelerator engine, showing its higher normalised performance and lower normalised energy consumption. Experime- tal results show up to 2.5 times performance increase compared with existing scratch-pad architectures and 5 times compared with cache architectures and energy decrease up to 1.9 and 3.9 times, respectively.