Parallel matrix multiplication on memristor-based computation-in-memory architecture

One of the most important constraints of today's architectures for data-intensive applications is the limited bandwidth due to the memory-processor communication bottleneck. This significantly impacts performance and energy. For instance, the energy consumption share of communication and memory access may exceed 80%. Recently, the concept of Computation-in-Memory (CIM) was proposed, which is based on the integration of storage and computation in the same physical location using a crossbar topology and non-volatile resistive-switching memristor technology. To illustrate the tremendous potential of CIM architecture in exploiting massively parallel computation while reducing the communication overhead, we present a communication-efficient mapping of a large-scale matrix multiplication algorithm on the CIM architecture. The experimental results show that, depending on the matrix size, CIM architecture exhibits several orders of magnitude higher performance in total execution time and two orders of magnitude better in total energy consumption than the multicore-based on the shared memory architecture.