(Presented by Minxuan Zhou, UCSD, on June 24th.)
Digital processing in-memory (PIM) is a promising technology that minimizes data movement overhead while enabling an extremely high degree of parallelism. These characteristics provide a great opportunity for accelerating emerging big-data applications. Such optimization requires that both software and hardware be considered in order to get an efficient and reliable solution. In this talk we first illustrate our software-hardware co-design methodology for digital PIM by using an example of graph processing workloads. We next introduce several new PIM-specific methods that improve the reliability of the system by efficiently managing its thermal issues. Our results show that it is possible to get orders of magnitude improvement in speed over the state of the art graph processing algorithms, while ensuring thermal and reliability constraints are met.
