Approximate In-memory computing on RERAMs

Computing systems have seen tremendous growth over the past few decades in their capabilities, efficiency, and deployment use cases. This growth has been driven by progress in lithography techniques, improvement in synthesis tools, architectures and power management. However, there is a growing disparity between computing power and the demands on modern computing systems. The standard Von-Neuman architecture has separate data storage and data processing locations. Therefore, it suffers from a memory-processor communication bottleneck, which is commonly referred to as the \u27memory wall\u27. The relatively slower progress in memory technology compared with processing units has continued to exacerbate the memory wall problem. As feature sizes in the CMOS logic family reduce further, quantum tunneling effects are becoming more prominent. Simultaneously, chip transistor density is already so high that all transistors cannot be powered up at the same time without violating temperature constraints, a phenomenon characterized as dark-silicon. Coupled with this, there is also an increase in leakage currents with smaller feature sizes, resulting in a breakdown of \u27Dennard\u27s\u27 scaling. All these challenges cannot be met without fundamental changes in current computing paradigms. One viable solution is in-memory computing, where computing and storage are performed alongside each other. A number of emerging memory fabrics such as ReRAMS, STT-RAMs, and PCM RAMs are capable of performing logic in-memory. ReRAMs possess high storage density, have extremely low power consumption and a low cost of fabrication. These advantages are due to the simple nature of its basic constituting elements which allow nano-scale fabrication. We use flow-based computing on ReRAM crossbars for computing that exploits natural sneak paths in those crossbars. Another concurrent development in computing is the maturation of domains that are error resilient while being highly data and power intensive. These include machine learning, pattern recognition, computer vision, image processing, and networking, etc. This shift in the nature of computing workloads has given weight to the idea of approximate computing , in which device efficiency is improved by sacrificing tolerable amounts of accuracy in computation. We present a mathematically rigorous foundation for the synthesis of approximate logic and its mapping to ReRAM crossbars using search based and graphical methods

The Kinetic Basis of Self-Organized Pattern Formation

In his seminal paper on morphogenesis (1952), Alan Turing demonstrated that different spatio-temporal patterns can arise due to instability of the homogeneous state in reaction-diffusion systems, but at least two species are necessary to produce even the simplest stationary patterns. This paper is aimed to propose a novel model of the analog (continuous state) kinetic automaton and to show that stationary and dynamic patterns can arise in one-component networks of kinetic automata. Possible applicability of kinetic networks to modeling of real-world phenomena is also discussed.Comment: 8 pages, submitted to the 14th International Conference on the Synthesis and Simulation of Living Systems (Alife 14) on 23.03.2014, accepted 09.05.201

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Evolutionary computation based on nanocomposite training: application to data classification

Research into novel materials and computation frameworks by-passing the limitations of the current paradigm, has been identified as crucial for the development of the next generation of computing technology. Within this context, evolution in materio (EiM) proposes an approach where evolutionary algorithms (EAs) are used to explore and exploit the properties of un-configured materials until they reach a state where they can perform a computational task. Following an EiM approach, this thesis demonstrates the ability of EAs to evolve dynamic nanocomposites into data classifiers. Material-based computation is treated as an optimisation problem with a hybrid search space consisting of configuration voltages creating an electric field applied to the material, and the infinite space of possible states the material can reach in response to this field. In a first set of investigations, two different algorithms, differential evolution (DE) and particle swarm optimisation (PSO), are used to evolve single-walled carbon nanotube (SWCNT) / liquid crystal (LC) composites capable of classifying artificial, two-dimensional, binary linear and non-linear separable and merged datasets at low SWCNT concentrations. The difference in search behaviour between the two algorithms is found to affect differently the composite’ state during training, which in turn affects the accuracy, consistency and generalisation of evolved solutions. SWCNT/LC processors are also able to scale to complex, real-life classification problems. Crucially, results suggest that problem complexity influences the properties of the processors. For more complex problems, networks of SWCNT structures tend to form within the composite, creating stable devices requiring no configuration voltages to classify data, and with computational capabilities that can be recovered more than several hours after training. A method of programming the dynamic composites is demonstrated, based on the reapplication of sequences of configuration voltages which have produced good quality SWCNT/LC classifiers. A second set of investigations aims at exploiting the properties presented by the dynamic nanocomposites, whilst also providing a means for evolved device encapsulation, making their use easier in out-of-the lab applications. Novel composites based on SWCNTs dispersed in one-part UV-cure epoxies are introduced. Results obtained with these composites support their choice for use in subsequent EiM research. A final discussion is concerned with evolving an electro-biological processor and a memristive processor. Overall, the work reported in the thesis suggests that dynamic nanocomposites present a number of unexpected, potentially attractive properties not found in other materials investigated in the context of EiM

On the development of slime mould morphological, intracellular and heterotic computing devices

The use of live biological substrates in the fabrication of unconventional computing (UC) devices is steadily transcending the barriers between science fiction and reality, but efforts in this direction are impeded by ethical considerations, the field’s restrictively broad multidisciplinarity and our incomplete knowledge of fundamental biological processes. As such, very few functional prototypes of biological UC devices have been produced to date. This thesis aims to demonstrate the computational polymorphism and polyfunctionality of a chosen biological substrate — slime mould Physarum polycephalum, an arguably ‘simple’ single-celled organism — and how these properties can be harnessed to create laboratory experimental prototypes of functionally-useful biological UC prototypes. Computing devices utilising live slime mould as their key constituent element can be developed into a) heterotic, or hybrid devices, which are based on electrical recognition of slime mould behaviour via machine-organism interfaces, b) whole-organism-scale morphological processors, whose output is the organism’s morphological adaptation to environmental stimuli (input) and c) intracellular processors wherein data are represented by energetic signalling events mediated by the cytoskeleton, a nano-scale protein network. It is demonstrated that each category of device is capable of implementing logic and furthermore, specific applications for each class may be engineered, such as image processing applications for morphological processors and biosensors in the case of heterotic devices. The results presented are supported by a range of computer modelling experiments using cellular automata and multi-agent modelling. We conclude that P. polycephalum is a polymorphic UC substrate insofar as it can process multimodal sensory input and polyfunctional in its demonstrable ability to undertake a variety of computing problems. Furthermore, our results are highly applicable to the study of other living UC substrates and will inform future work in UC, biosensing, and biomedicine

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

Reservoir Computing in Materio

Reservoir Computing first emerged as an efficient mechanism for training recurrent neural networks and later evolved into a general theoretical model for dynamical systems. By applying only a simple training mechanism many physical systems have become exploitable unconventional computers. However, at present, many of these systems require careful selection and tuning by hand to produce usable or optimal reservoir computers. In this thesis we show the first steps to applying the reservoir model as a simple computational layer to extract exploitable information from complex material substrates. We argue that many physical substrates, even systems that in their natural state might not form usable or "good" reservoirs, can be configured into working reservoirs given some stimulation. To achieve this we apply techniques from evolution in materio whereby configuration is through evolved input-output signal mappings and targeted stimuli. In preliminary experiments the combined model and configuration method is applied to carbon nanotube/polymer composites. The results show substrates can be configured and trained as reservoir computers of varying quality. It is shown that applying the reservoir model adds greater functionality and programmability to physical substrates, without sacrificing performance. Next, the weaknesses of the technique are addressed, with the creation of new high input-output hardware system and an alternative multi-substrate framework. Lastly, a substantial effort is put into characterising the quality of a substrate for reservoir computing, i.e its ability to realise many reservoirs. From this, a methodological framework is devised. Using the framework, radically different computing substrates are compared and assessed, something previously not possible. As a result, a new understanding of the relationships between substrate, tasks and properties is possible, outlining the way for future exploration and optimisation of new computing substrates