Faster and cheaper computers have been constantly demanding technological and architectural improvements. However, current technology is suffering from three technology walls: leakage wall, reliability wall, and cost wall. Meanwhile, existing architecture performance is also saturating due to three well-known architecture walls: memory wall, power wall, and instruction-level parallelism (ILP) wall. Hence, a lot of novel technologies and architectures have been introduced and developed intensively. Our previous work has presented a comprehensive classification and broad overview of memory-centric computer architectures. In this article, we aim to discuss the most important classes of memory-centric architectures thoroughly and evaluate their advantages and disadvantages. Moreover, for each class, the article provides a comprehensive survey on memory-centric architectures available in the literature.@en

A novel type of hardware accelerators called automata processors (APs) have been proposed to accelerate finite-state automata. The bone structure of an AP is a hierarchical routing matrix that connects many memory arrays. With this structure, an AP can process an input symbol every clock cycle, and hence achieve much higher performance compared to conventional architectures. However, the design automation for the APs is not well researched. This article proposes a fully automated tool named APmap for mapping the automata to APs that use a two-level routing matrix. APmap first partitions a large automaton into small graphs and then maps them. Multiple transformations are applied to the automaton by APmap to meet hardware constraints. The experiments on a standard benchmark suite show that our approach leads to around 19% less storage utilization compared to state-of-the-art.

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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 computational resources to the memory, and significantly reducing the communication. Memristive devices are promising enablers of a such CIM paradigm, as they are able to support both storage and computing. This paper shows the power of memristive device based CIM paradigm in enabling new efficient application-specific architectures as well as efficient implementations of some known domain-specific architectures. In addition, the paper discusses the potential applications that could benefit from such paradigm and highlights the major challenges.@en

Technological and architectural improvements have been constantly required to sustain the demand of faster and cheaper computers. However, CMOS down-scaling is suffering from three technology walls: leakage wall, reliability wall, and cost wall. On top of that, a performance increase due to architectural improvements is also
gradually saturating due to three well-known architecture walls: memory wall, power wall, and instruction level parallelism (ILP) wall. Hence, a lot of research is focusing on proposing and developing new technologies and architectures. In this article, we present a comprehensive classification of memory-centric computing architectures; it is based on three metrics: computation location, level of parallelism, and used memory technology. The classification not only provides an overview of existing architectures with their pros and cons but also unifies the terminology that uniquely identifies these architectures and highlights the potential future architectures that can be further explored. Hence, it sets up a direction for future research in the field.@en

Memristive devices have the potential to reduce the memory access bottleneck in conventional computer architectures. However, memristive devices also suffer from low endurance and large resistance variation. To address these problems, we present a robust logic scheme named Enhanced Scouting Logic (ESL). It produces logic operation results within the peripheral circuit of the memory array. During the execution of logic operations, the resistance states of memristive devices do not change and hence do not affect the memristor lifetime. ESL senses the resistance of input memristive devices via two different paths when different operations such as AND and OR are performed. These different paths guarantee the operation correctness even under large resistance variations. We verified ESL using SPICE simulations and Monte Carlo analysis. Our simulation results show that ESL is more robust as compared with state-of-the-art logic schemes. @en

Emerging computing applications (such as big-data and Internet-of-things) are extremely demanding in terms of storage, energy and computational efficiency, while today’s architectures and device technologies are facing major challenges making them incapable to meet these demands. Computation-in-Memory (CIM) architecture based on memristive devices is one of the alternative computing architectures being explored to address these limitations. Enabling such architectures relies on the development of efficient memristive circuits being able to perform logic and arithmetic operations within the non-volatile memory core. This paper addresses memristive circuit designs for CIM architectures. It gives a complete overview of all designs, both for logic as well as arithmetic operations, and presents the most popular designs in details. In addition, it analyzes and classifies them, shows how they result in different CIM flavours and how these architectures distinguish themselves from traditional ones. The paper also presents different potential applications that could significantly benefit from CIM architectures, based on their kernel that could be accelerated. @en

Today's computing architectures and device technologies are unable to meet the increasingly stringent demands on energy and performance posed by emerging applications. Therefore, alternative computing architectures are being explored that leverage novel post-CMOS device technologies. One of these is a Computation-in-Memory architecture based on memristive devices. This paper describes the concept of such an architecture and shows different applications that could significantly benefit from it. For each application, the algorithm, the architecture, the primitive operations, and the potential benefits are presented. The applications cover the domains of data analytics, signal processing, and machine learning. @en

In-memory computing is a promising computing paradigm due to its capability to alleviate the memory bottleneck. It has even higher potential when implemented usingmemristive devices ormemristors with various beneficial characteristics such as nonvolatility, high scalability, near-zero standby power consumption, high density, and CMOS compatibility. Exploring in-memory computing architectures in the combination withmemristor technology is still in its infancy phase. Therefore, it faces challenges with respect to the development of the devices, circuits, architectures, compilers and applications. This thesis focuses on exploring and developing in-memory computing in terms of architectures (including classification, limited schemes of instruction set,micro-architecture, communication and controller, as well as automation and simulator), and circuits (including logic synthesis flow and interconnect network schemes).@en

Today's computing architectures suffer from the three well-known bottlenecks, which are the memory, the power and the instruction-level parallelism walls. Emerging non-volatile technologies, such as memristor, enable new resistive architectures that alleviate at least two of such bottlenecks, as they can process data within the memory with almost no leakage. In this paper, we propose a novel resistive computing architecture by extending a conventional architecture with a resistive based Computation-In-Memory accelerator (CIMX). We evaluate the delay, energy and area of the conventional and CIMX architecture using an analytical model and a simulation framework. The results (both based on the analytical model and simulation framework) show that the proposed architecture achieves at least one order of magnitude improvement in terms of performance, area, and energy efficiency for the considered benchmarks.@en

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 multiple pipelined stages, making it possible to process several input sequences in parallel by using time-division multiplexing. We use a new resistive RAM based AP (instead of known DRAM or SRAM based) to illustrate the potential of our method. The experimental results show that our approach increases the throughput by almost a factor of 2 at a cost of marginal area overhead.@en

Alternatives to CMOS logic circuit implementations are under research for future scaled electronics. Memristor crossbar-based logic circuit is one of the promising candidates to at least partially replace CMOS technology, which is facing many challenges such as reduced scalability, reliability, and performance gain. Memristor crossbar offers many advantages including scalability, high integration density, nonvolatility, etc. The state-of-the-art for memristor crossbar logic circuit design can only implement simple and small circuits. This paper proposes a mapping methodology of large Boolean logic circuits on memristor crossbar. Appropriate place-and-route schemes, to efficiently map the circuits on the crossbar, as well as several optimization schemes are also proposed. To illustrate the potential of the methodology, a multibit adder and other nine more complex benchmarks are studied; the delay, area and power consumption induced by both crossbar and its CMOS control part are evaluated.@en

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 them incapable of providing the demanded computing power with tight constraints. This motivates the need for the exploration of novel architectures based on new device technologies; not only to sustain the financial benefit of technology scaling, but also to develop solutions for extremely demanding emerging applications. This paper presents two computation-in-memory based accelerators making use of emerging memristive devices; they are Memristive Vector Processor and RRAM Automata Processor. The preliminary results of these two accelerators show significant improvement in terms of latency, energy and area as compared to today's architectures and design.@en

The memristor is an emerging technology which is triggering intense interdisciplinary activity. It has the potential of providing many benefits, such as energy efficiency, density, reconfigurability, nonvolatile memory, novel computational structures and approaches, massive parallelism, etc. These characteristics may lead to deeply revise existing computing and storage paradigms. This paper presents a comprehensive overview of memristor technology and its potential to design a new computational paradigm.@en

Today's computing systems suffer from a memory/communication bottleneck, resulting in high energy consumption and saturated performance. This makes them inefficient in solving data-intensive applications at reasonable cost. Computation-In-Memory (CIM) architecture, based on the integration of storage and computation in the same physical location using non-volatile memristor crossbar technology, offers a potential solution to the memory bottleneck. An efficient interconnect network is essential to maximize CIM's architectural performance. This paper presents three interconnect network schemes for CIM architecture; these are (1) CMOS-based, (2) memristor-based and (3) hybrid cmos/memristor interconnect network scheme. To illustrate the feasibility of such schemes, a CIM parallel adder is used as a case study. The results show that the hybrid interconnect network scheme achieves a higher performance in comparison with the CMOS-based and memristor-based interconnect scheme in terms of delay, energy and area.@en

As today's CMOS technology is scaling down to its physical limits, it suffers from major challenges such as increased leakage power and reduced reliability. Novel technologies, such as memristors, nanotube, and graphene transistors, are under research as alternatives. Among these technologies, memristor is a promising candidate due to its great scalability, high integration density and near-zero standby power. However, memristor-based logic circuits are facing robustness challenges mainly due to improper values of design parameters (e.g., OFF/ON ratio, control voltages). Moreover, process variation, sneak path currents and parasitic resistance of nanowires also impact the robustness. To realize a robust design, this paper formulates proper constraints for design parameters to guarantee correct functionality of logic gates (e.g., AND). Our proposal is verified with SPICE simulations while taking both device variation and parasitic effects into account. It is observed that the errors due to analytical parameter constraints are typically within 4.5% as compared to simulations.@en

Memristor technology is a promising alternative to CMOS due to its high integration density, near-zero standby power, and ability to implement novel resistive computing. One of the major limitations of these architectures is the limited endurance of memristor devices, especially when a logic gate requires multiple steps/switching to execute the logic operations. To alleviate the endurance requirement and improve the performance, we present a novel logic design style, called scouting logic that executes any logic gate by only reading the memristor devices and without changing their states. Hence, no impact on the memristors' endurance. The proposed design is implemented using two styles (current and voltage based). To illustrate the performance of scouting logic based designs, the area, delay, and power consumption are analyzed and compared with state-ofthe- art. The results show that scouting logic improves the delay and power consumption by at least a factor of 2.3, while having similar or less area overhead. Finally, we discuss the potential applications and challenges of scouting logic.@en

Today's computer architectures suffer from many challenges, such as the near end of CMOS downscaling, the memory/communication bottleneck, the power wall, and the programming complexity. As a consequence, these architectures become inefficient in solving big data problems or general data intensive applications. Computation-in-memory (CIM) is a novel architecture that tries to solve/alleviate the impact of these challenges using the same device (i.e., the memristor) to implement the processor and memory in the same physical crossbar. In order to analyze its feasibility in depth, this paper proposes two memristor implementations of a data intensive arithmetic application (i.e., parallel addition). To the best of our knowledge, this is the first paper that considers the cost of the entire architecture including both crossbar and its CMOS controller. The results show that CIM architecture in general and the CIM parallel adder in particular have a high scalability. CIM parallel adder achieves at least two orders of magnitude improvement in energy and area in comparison with a multicore-based parallel adder. Moreover, due to a wide variety of memristor design methods (such as Boolean logic), tradeoffs can be made between the area, delay, and energy consumption.@en

Many emerging technologies are under investigation to realize alternatives for future scalable electronics. Memristor is one of the most promising candidates due to memrsitor’s non-volatility, high integration density, near-zero standby power consumption, etc. Memristors have been recently utilized in non-volatile memory, neuromorphic system, resistive computing architecture, and FPGA to name but a few. An FPGA typicallyconsists of configurable logic blocks (CLBs), programmableinterconnects, configuration, and block memories. Most of therecent work done was focused on using memristor to build FPGA interconnects and memories. This paper proposes two novel FPGA implementations that utilize memristor-based CLBs and their corresponding automatic design flow. To illustrate the potential of the proposed implementations, they are benchmarked using Toronto 20, and compared with the state-of-the-art in terms of area and delay. The experimental results show that both the area (up to 4.24x) and delay (up to 1.46x) of the novel FPGAs
are very promising.@en

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

Memristors are emerging devices with huge potentials. It has been shown that they can be used not only to design non-volatile memories, but also logic circuits. In the latter, memristor devices are stacked on a CMOS circuit which generates the required control signals needed by the memristors to perform the required functionality. This paper sets a step towards automating this process; it proposes a generic synthesis framework to map logic circuits on memristor crossbar. The framework takes HDL descriptions as input and generates both its memristor circuitry and its associated CMOS control. The framework consists of three phases: (i) netlist generation, (ii) partition and mapping, and (iii) placement and routing. To illustrate the framework, a combinational and a sequential circuit are investigated. The results are validated using HSPICE simulations.@en