Weebit - 2021and beyond, page-9216

I received via WBT and EKuret this reference on RRAM
https://nanoscalereslett.springeropen.com/articles/10.1186/s11671-020-03299-9
The most relevant part for me was the section below. I hope you find it useful.

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Applications of RRAM

RRAM is seen as one of the standout candidates among the emerging memory technologies that has the potential for reforming the memory hierarchy primarily due to its high speed, the capability of non-volatile data storage, enhanced storage density and logic computing function. The various novel applications of RRAM are discussed in this section.

>Non-volatile Logic

The instruction codes and the data are transferred by making use of buses between various units in a computer system having von Neumann architecture because of the separate computing and memory unit. This data transferring process results increased energy consumption and time delay, which is commonly referred to as ‘von Neumann bottleneck’. For reducing the impact of von Neumann bottleneck [156], the computing process which utilizes RRAM crossbar array is suggested which alters the memory and computing operations in the same core. In addition, to obtain high integration density and low cost [157], two-terminal compact device structure of RRAM and its 4F² array architecture are highly beneficial. For example, to obtain simple Boolean logic functions such as ‘logic NOT’, ‘logic AND’, and ‘logic OR’, we require multiple transistors and each single transistor takes 8−10F² area. These logic functions can be realized by making use of two or three RRAM cells, resulting in total approximate area of around 10F² only [158].

Till date, several methods have been suggested for realizing Boolean logic functions [159, 160]. Boolean computing is significantly more established compared to existing non-Boolean computing paradigms such as neuromorphic computing and quantum computing. Therefore, energy and cost-efficiency of CPU or MCU can be enhanced without the need to develop new algorithms or software, although there is still a lack of technical solution on how to implement complex computing tasks in a crossbar array. Thus, most of research to date focusses on only basic logic level demonstration as it becomes quite complex to implement a whole computing unit using RRAM array.

Neuromorphic Computing

To overcome ‘von Neumann bottleneck’, one of the effective ways is brain-inspired neuromorphic computing which has shown promising potential in a wide range of complex and cognitive tasks like visual/audio recognition, self-driving, and real-time big-data analytics. Compared to CMOS-based neuromorphic network, neuromorphic computing based on RRAM-array offers advantages in terms of on-chip weight storage, online training, and scaling up to much larger array size [161–163]. In addition, the processing speed of RRAM improves by three orders of magnitude, whereas the power consumption rate is reduced by four orders of magnitude [164].

For realizing hardware-implemented neuromorphic computing paradigms, two methods are suggested: one among the strategies mimics the structure and working mechanism of biological neural networks while the other method works on accelerating the existing artificial neural network (ANN) algorithms. In a neural network, a synapse is used to transfer spikes between different neurons in addition to storing information about the transferring weights. The information regarding weights can be acquired through certain learning rules such as spike-time-dependent plasticity (STDP) and spike-rate-dependent plasticity (SRDP) [165–167]. Although some of the works reported in the literature have tried to emulate such learning rules on RRAM devices, it is however quite complicated to extend such types of bioinspired learning rules to a complex task as the theoretical algorithm is still lacking.

A practically viable approach is to map an ANN to a RRAM-based neuromorphic network directly. Some advanced tasks such as pattern and speech recognition have been demonstrated based on this method [166–169]. Although very promising, RRAM-based synapse is still far from being applied as various issues such as material optimization, variation suppressing, control circuit design, architecture, and algorithms design for analog computing need to be addressed effectively.

Security Application

The security aspect has become more prominent with rapid developments in the field of information technology; thus, there is a need for hardware-based security-integrated circuits. In contrast to security circuits based on CMOS logic which exploits the random nature of the semiconductor manufacturing process, security circuits based on RRAM are more robust to attacks of various types due to its completely random switching mechanisms [170, 171]. It must be noted that for security applications, larger variation of RRAM device parameters such as random telegraph noise (RTN), resistance variations and probabilistic switching is desirable, which is quite different from memory applications that require a smaller degree of variation among numerous parameters.

A novel security feature commonly referred to as physical unclonable function (PUF) [172], based on RRAM is proposed for device authentication (strong PUF) and key generation (weak PUF) applications. Significantly larger number of input-output pairs [also called challenge-response pair (CRP) are required for strong PUF, while only a small amount of CRPs of extremely higher reliability are required for weak PUF [173]. Although, PUFs based on RRAM have demonstrated remarkable performance; however, still more practical demonstrations and further evaluations are required to work out the maturity of this new primitive within the field of hardware security.

Non-volatile SRAM

Volatile memory technologies like SRAM and DRAM may consume over half of the static power within the current mobile SoC chips. Thus, to attain fast parallel memory operations, reduced area and low-energy consumption, RRAM-based non-volatile SRAM (nvSRAM) was proposed [174] in which two RRAM cells are stacked on eight transistors, forming an 8T2R structure. Also, non-volatile ternary content-addressable memory (TCAM) having 4T2R cell structure [175] and non-volatile flip flops having reduced stress time and write power based on RRAM have been demonstrated recently [176].

Challenges and Future Outlook

During the past several years, research in the field of emerging memory technologies has grown significantly and several prototype RRAM products have been developed demonstrating the potential for high-speed and low-power embedded memory applications. RRAM is one of the most promising memory technologies because of the advantages of simple structure, compatibility with the existing CMOS technology, good switching speed and ability to scale to the smallest dimensions. As a matter of fact, currently the Flash memory technology is facing difficulties to reduce to lower dimensions and as such RRAM is emerging as a potential replacement especially for fast operation and medium size storage density memory applications.
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i note that another stock listed on ASX makes use of neuromorphic computing. Also, Renesas appear to be working on something called a dynamically reconfigurable processor.

https://www.renesas.com/us/en/document/whp/embedded-ai-accelerator-drp-ai?r=1496691
It looks like there is some synergy down the track with RRam and overcoming the bottleneck of traditional Von Neumann CPUs as the potential of compute in memory is investigated with RRAM.
IMO. DYOR.