Splitting functions in code management on scratchpad memories

As the number of cores increases, cache-based memory hierarchy is becoming a major problem in terms of the scalability and energy consumption. Software-managed scratchpad memories (SPM) is a scalable alternative to caches, but the benefit comes at the cost of explicit management of data. For instance, an instruction SPM needs a code management techniques to load code blocks to the SPM. This paper presents a technique to split functions into smaller functions, to break away with the fundamental limitations of function-level code management. Our function-splitting technique is able to generate more efficient mappings by modifying the characteristics of functions to be more suitable for function-level code management. We propose two optimization policies to improve performance and reduce size respectively. The performance optimization policy improves performance by 16% on average, which can only be achieved by using 20% more SPM space if without function-splitting The size optimization policy can reduce the minimum SPM size requirement by 31% while increasing only 7% execution time.