In recent years, we are witnessing a trend toward in-memory computing for future generations of computers that differs from traditional von-Neumann architecture in which there is a clear distinction between computing and memory units. Considering that data movements between the central processing unit (CPU) and memory consume several orders of magnitude more energy compared to simple arithmetic operations in the CPU, in-memory computing will lead to huge energy savings as data no longer needs to be moved around between these units. In an initial step toward this goal, new non-volatile memory technologies, e.g., resistive RAM (ReRAM) and phase-change memory (PCM), are being explored. This has led to a large body of research that mainly focuses on the design of the memory array and its peripheral circuitry. In this article, we mainly focus on the tile architecture (comprising a memory array and peripheral circuitry) in which storage and compute operations are performed in the (analog) memory array and the results are produced in the (digital) periphery. Such an architecture is termed compute-in-memory-periphery (CIM-P). More precisely, we derive an abstract CIM-tile architecture and define its main building blocks. To bridge the gap between higher-level programming languages and the underlying (analog) circuit designs, an instruction-set architecture is defined that is intended to control and, in turn, sequence the operations within this CIM tile to perform higher-level more complex operations. Moreover, we define a procedure to pipeline the CIM-tile operations to further improve the performance. To simulate the tile and perform design space exploration considering different technologies and parameters, we introduce the fully parameterized first-of-its-kind CIM tile simulator and compiler. Furthermore, the compiler is technology-aware when scheduling the CIM-tile instructions. Finally, using the simulator, we perform several preliminary design space explorations regarding the three competing technologies, ReRAM, PCM, and STT-MRAM concerning CIM-tile parameters, e.g., the number of ADCs. Additionally, we investigate the effect of pipelining in relation to the clock speeds of the digital periphery assuming the three technologies. In the end, we demonstrate that our simulator is also capable of reporting energy consumption for each building block within the CIM tile after the execution of in-memory kernels considering the data-dependency on the energy consumption of the memory array. All the source codes are publicly available.

Complementary Metal Oxide Semiconductor (CMOS) technology has been scaled down over the last forty years making possible the design of high-performance applications, following the predictions made by Gordon Moore and Robert H. Dennard in the 1970s. However, there is a growing concern that device scaling, while maintaining cost-effective production, will become infeasible below a certain feature size. In parallel, emerging applications including Internet-of-Things (IoT) and big data applications present high demands in terms of storage and computing capability, combined with challenging constraints in terms of size, power consumption and response latency. In this scenario, memristive devices have become promising candidates to complement the CMOS technology due to their CMOS manufacturing process compatibility, great scalability and high density, zero standby power consumption and their capacity to implement high density memories as well as new computing paradigms. Despite these advantages, memristive devices are also susceptible to manufacturing defects that may cause unique faulty behaviors that are not seen in CMOS, increasing significantly the complexity of test procedures. This paper provides a review about the manufacturing process of memristives devices, focusing on Valence Change Mechanism (VCM)-based memristive devices, and a comparative analysis of the CMOS and memristive device manufacturing processes. Moreover, this paper identifies possible manufacturing failure mechanisms that may affect these novel devices, completing the list of the already known mechanisms, and provides a discussion about possible faulty behaviors. Note that the identification of these mechanisms provides insights regarding the possible memristive devices’ defective behaviors, enabling to derive more accurate fault models and consequently, more suitable test procedures.

Memristive devices can be exploited for memory as well as logic operation paving the way for non von-Neumann Computation-In-Memory architectures. To validate the potential of such architectures accurate compact models for the memristive devices are required. As a standard device is not available, evaluating the performance of such an architecture is ambiguous. This paper proposes a flexible model for bipolar, filamentary switching, redox-based memristive devices. The model does catch both the device resistance ratio as well as the nonlinearity of the switching kinetics. It is used to perform design exploration for three memristive based circuit design (IMPLY, MAGIC and CRS) for computation-in-memory architectures.