Increasing on-chip memory space utilization for embedded chip multiprocessors through data compression

Minimizing the number of off-chip memory references is very important in chip multiprocessors from both the performance and power perspectives. To achieve this the distance between successive reuses of the same data block must be reduced. However, this may not be possible in many cases due to data dependences between computations assigned to different processors. This paper focuses on software-managed on-chip memory space utilization for embedded chip multiprocessors and proposes a compression-based approach to reduce the memory space occupied by data blocks with large inter-processor reuse distances. The proposed approach has two major components: a compiler and an ILP (integer linear programming) solver. The compiler's job is to analyze the application code and extract information on data access patterns. This access pattern information is then passed to our ILP solver, which determines the data blocks to compress/decompress and the times (the program points) at which to compress/decompress them. We tested the effectiveness of this ILP based approach using access patterns extracted by our compiler from application codes. Our experimental results reveal that the proposed approach is very effective in reducing power consumption. Moreover, it leads to a lower energy consumption than an alternate scheme evaluated in our experiments for all the test cases studied.