The Power of Computation-in-Memory Based on Memristive Devices

Conventional computing architectures and the CMOS technology that they are based on are facing major challenges such as the memory bottleneck making the memory access for data transfer a major killer of energy and performance. Computation-in-memory (CIM) paradigm is seen as a potential alternative that could alleviate such problems by adding...

Time-division Multiplexing Automata Processor

Automata Processor (AP) is a special implementation of non-deterministic finite automata that performs pattern matching by exploring parallel state transitions. The implementation typically contains a hierarchical switching network, causing long latency. This paper proposes a methodology to split such a hierarchical switching network into...

Memristive devices for computation-in-memory

CMOS technology and its continuous scaling have made electronics and computers accessible and affordable for almost everyone on the globe; in addition, they have enabled the solutions of a wide range of societal problems and applications. Today, however, both the technology and the computer architectures are facing severe challenges/walls making...

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<br/>access may exceed 80%. Recently, the concept of...

Skeleton-based design and simulation flow for Computation-in-Memory architectures

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...