In this paper, we briefly discuss a new proposed architecture, Computation-In-Memory (CIM) architecture, that targets specifically data-intensive applications. The architecture consists of the interwoven placement of computing and storage units, which are physically tightly integrated together inside a non-volatile memristor crossbar. The architecture has the potential of improving the energy-delay product, computing efficiency and performance per area by at least two orders of magnitude with respect to conventional CMOS architectures.@en

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 communicationefficient 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.@en

Memristor-based Computation-in-Memory is one of the emerging architectures proposed to deal with Big Data problems. The design of such architectures requires a radically new automatic design flow because the memristor is a passive device that uses resistance to encode its logic value. This paper proposes a design flow for mapping parallel algorithms on the CIM architecture. Algorithms with similar data flow graphs can be mapped on the crossbar using the same template containing scheduling, placement, and routing information; this template is named skeleton. By configuring such a skeleton with different pre-designed circuits, we can build CIM implementations of the corresponding algorithms in that class. This approach does not only map an algorithm on a memristor crossbar, but also gives an estimation of its performance, area, and energy consumption. It also supports user-defined constraints and parallel SystemC simulation. Experimental results demonstrate the feasibility and the potential of the approach.@en