Graphene has long been considered a revolutionary material for the field of electronics due to its remarkable set of electronic properties, standing as a very promising candidate for the post-silicon era. However, it is not just a silicon replacement, but rather an enabling material for different computing paradigms. In this work, we investigate the use of graphene in devices and circuits that are employed for the realisation of computing architectures and systems. More specifically, we focus on impactful key applications such as conventional computing and Boolean logic, high-radix computing and multi-valued logic, memristive devices and in-memory-computing, neuromorphic applications, quantum computing and photonics. Additionally, taking into consideration the state-of-the-art as well as the existing graphene-related challenges that are still present, this work attempts to assess the possible future development of graphene-based devices, circuits and systems in each of the aforementioned fields and to propose a coarse yet directive roadmap for the material's future in computing architectures.

Quantum-dot Cellular Automata (QCA) provide very high scale integration potential, very high switching frequency, and have extremely low power demands, which make the QCA technology quite attractive for the design and implementation of large-scale, high-performance nanoelectronic circuits. However, state-of-the-art QCA circuit designs were not derived by following a set of universal design rules, as is the case of CMOS circuits, and, as a result, it is either impossible or very difficult to combine QCA circuit blocks in effective large-scale circuits. In this paper, we introduce a novel automated design methodology, which builds upon a QCA specific universal design rules set. The proposed methodology assumes the availability of a generic QCA crossbar architecture and provides the means to customize it in order to implement any given logic function. The programming principles and the flow of the proposed automated design tool for crossbar QCA circuits are described analytically and we apply the proposed automated design method for the design of both combinatorial and sequential circuits. The obtained designs demonstrate that the proposed method is functional, easy to use, and provides the desired QCA circuit design unification.

Memristor networks are capable of low-power and massive parallel processing and information storage. Moreover, they have presented the ability to apply for a vast number of intelligent data analysis applications targeting mobile edge devices and low power computing. Beyond the memory and conventional computing architectures, memristors are widely studied in circuits aiming for increased intelligence that are suitable to tackle complex problems in a power and area efficient manner, offering viable solutions oftenly arriving also from the biological principles of living organisms. In this paper, a memristive circuit exploiting the dynamics of oscillating networks is utilized for the resolution of very popular and NP-complete logic puzzles, like the well-known “Sudoku”. More specifically, the proposed circuit design methodology allows for appropriate usage of interconnections' advantages in a oscillation network and of memristor's switching dynamics resulting to logic-solvable puzzle-instances. The reduced complexity of the proposed circuit and its increased scalability constitute its main advantage against previous approaches and the broadly presented SPICE based simulations provide a clear proof of concept of the aforementioned appealing characteristics.

Stochastic Resonance (SR) is a nonlinear system specific phenomenon, which was demonstrated to lead to system unexpected (counter-intuitive) performance improvements under certain noise conditions. Memristor, on the other hand, is a fundamentally nonlinear circuit element, thus susceptible to benefit from SR, which recently came in the spotlight of the emerging technologies potential candidates. However, at this time, the variability exhibited by manufactured memristor devices within the same array constitutes the main hurdle in the road towards the commercialisation of memristor-based memories and/or computing units. Thus, in this paper, memristor SR effects are explored, assuming various memristor models, and SR-based memristance range enhancement, tolerant to device-to-device variability, is demonstrated. Our experiments reveal that SR can induce significant RMAX/RMIN ratio increase under up to 60% variability, getting as high as 3.4× for 29 dBm noise power.