Chemical Computing

PREFACE.
At the invisible frontiers where physics, chemistry, and biology converge, a new language emerges to describe life—not as a rigid succession of crystalline structures, but as a continuous flow of conformational possibilities. This book is born of a shared conviction between its co-authors—Joachim Frank, awarded the Nobel Prize in Chemistry in 2017 and pioneer of single-particle cryo-electron microscopy, and scientist Luis David Fernández Zambrano, legal philosopher and Director of the Chair for Culture of Peace and Human Rights at the Faculty of Social Sciences of the University of Buenos Aires—that contemporary science requires not only precision, but also sensitivity; not only algorithms, but interdisciplinary intuition.

From Conformational Horizons to Energetic Truths is not just another treatise on structural biology. It is a critical cartography of the challenges, boundaries, and possibilities that shape today’s efforts to reconstruct free energy landscapes in the context of single-particle cryo-EM, in a world where it is no longer enough to know a molecule’s “structure,” but rather the paths it follows, the barriers it overcomes, and the energetic truths revealed by its motion.

Inspired by natural metaphors—like a rolling meadow where a flock of sheep represents the individual particles captured by cryo-EM—this book transitions from visual poetry to mathematical physics with ease. In that meadow, each curve of the terrain symbolizes a possible conformation, each valley an energy minimum, and each hill a barrier that limits molecular movement. From this didactic image, we unfold a novel scientific architecture: a set of integrated tools that transform the promise of cryo-EM into high-fidelity maps.

The first challenge we address is uneven sampling. In regions with low data density, where few particles have been recorded, the risk of overestimating or underestimating their relevance is high. We propose dynamic baskets—adaptive collection devices that move toward scarcely explored zones—as an analogy for adaptive sampling, capable of increasing coverage without disproportionately increasing the experimental burden. These strategies are inspired by active data acquisition approaches and are complemented by enrichment techniques using targeted labels or ligand condition manipulation.

Secondly, we confront the computational and statistical challenges of processing millions of molecular projections in high-dimensional spaces. Through swarms of “parallel drones”—a metaphor for CPU+GPU architectures working in tandem—we avoid memory bottlenecks that have historically limited large-scale use of manifold embedding. At the statistical level, we move from discrete histograms to smooth distributions, implementing hierarchical Bayesian inference with physical regularization, enabling the capture of real uncertainty in data-scarce regions while respecting fundamental thermodynamic principles.

One of the most innovative contributions of this volume is the formulation of new physico-chemical equations, designed specifically to reduce error margins in energy landscape reconstruction. We introduce adaptive corrections to the Boltzmann distribution, enriched Fokker–Planck models with flows directed toward regions of interest, and hybrid free energy functionals that integrate MD simulations (such as MDSPACE) with experimental data. These mathematical frameworks not only improve precision but elevate the analysis to an explainable and reproducible dimension.

To experimentally validate the inferred pathways, the book presents a powerful fusion of hybrid data: techniques such as single-pair FRET, cross-linking mass spectrometry (XL-MS), and vitrification-based thermal analysis help confirm, correct, or discard illusory valleys. The inclusion of a mixed Q-metric synthesizes the coherence between computational predictions and empirical evidence, providing a robust validation criterion.

Equally significant is the reflection on scientific ethics and traceability. In a time when data access and methodological transparency are as critical as the discoveries themselves, we examine open data management policies promoted by platforms such as EMDB and EMPIAR. We advocate for the necessity of sharing not only final reconstructions, but also raw micrographs, classification parameters, and analytical decisions that shape the final model.

This deeply interdisciplinary book is also a tribute to creative collaboration between the hard sciences and the social sciences. At the intersection of cryo-EM and the phenomenology of the invisible, of free energy and interpretive freedom, a transformative pedagogy unfolds—one that engages with education, the philosophy of knowledge, and human rights. As Luis David Fernández Zambrano insightfully suggests, molecular structures also have a history—they travel paths, encounter obstacles, and make decisions that resonate with the human condition.

Ultimately, what we offer is not merely a scientific roadmap, but an invitation: to contemplate matter not as something static, but as an open system of possible transformations. To listen to the vibrant language of molecules. To map, with precision and poetry, the frontier between the probable and the true.

From Conformational Horizons to Energetic Truths is, in essence, an intellectual and technical journey toward a more honest and complete understanding of molecular motion—and a testament to what can happen when a Nobel laureate in chemistry and a philosopher of law come together to reconstruct the invisible landscapes of life.
Atte, Luis David Fernández Zambrano.
Institutional Pages:
https://www.nobelprize.org/prizes/chemistry/2017/frank/facts/

https://biology.columbia.edu/content/joachim-frank
https://magazine.columbia.edu/article/joachim-frank-wins-nobel-prize-chemistry


WORDS OF GRATITUDE
To my mentors: Adolfo Pérez Esquivel, Nobel Peace Prize Laureate (1980); Santos Vera Sandoval; Martha Minow; Román Faustino Fernández Adriano; Rosa María Zambrano Encalada; Luis Amparo Grimaldo Zapata; Tomás Várnagy; Sonia Winer; Daniel Elía; Josefina Miró Quesada Gayoso; Jhanela Brigite Aguirre Obando; Wilfredo Rueda Zegarra; Laura Mercedes Grimaldo Hidalgo; Ángel Atuncar Gutiérrez; Teresa Zamudio Ojeda; Richard Lovera Herrera; Miuller Barrera Trujillo; Mario Miranda Morales; Jesús Chuchón Vilca; Ítala Andrea Preguntegui Garrafa; Jesús Domingo Mavila Salón; Isabel Dafne Márquez Galarza; Manuel Raymundo Centeno Caffo; Orlando Vera Sandoval; Miguel Ángel Ciuro Caldani; Jesús Ruiz; Enrique Fidel Verástegui Peláez; Midori Estefanía Zuazo Aulla; Francisco Miró Quesada Cantuarias; Federico Fernández Adriano; Paula Olivia Rapetti; Rabbi Daniel Chapán; Olga Sandoval Julián; and Luis Alberto Sánchez Sánchez—thank you for your wisdom, compassion, and enduring inspiration.

To my parents—both earthly and spiritual—and to my beloved friends and relatives, including José Luis Pérez Chumpitaz and Guillermo Martín Morales Zambrano, whose unwavering support has sustained me through the most challenging times: may God bless you abundantly on your journeys.

I wish to express special gratitude to Luis Amparo Grimaldo Zapata, Ángel Atuncar Gutiérrez, Federico Fernández Adriano, my spiritual guide Santos Vera Sandoval—The King of Lambayeque—and José Campos Saavedra, whose guidance continues to illuminate our path from the heavens during these trying times.

My heartfelt appreciation extends to my distinguished friend and esteemed colleague, Joachim Frank, a biochemist at Columbia University, who won the 2017 Nobel Prize in Chemistry for his contribution to the development of a method for creating high-resolution 3D images of biomolecules such as proteins, lipids, and ribosomes. He shares the prize with Richard Henderson of the MRC Laboratory of Molecular Biology in Cambridge, UK, and Jacques Dubochet of the University of Lausanne, Switzerland. The trio’s technique, known as cryo-electron microscopy, is widely recognized for having revolutionized biological research. “This method has taken biochemistry into a new era,” proclaimed the official award announcement from the Royal Swedish Academy of Sciences. “In recent years, scientific literature has been filled with images of everything from proteins that cause antibiotic resistance to the surface of the Zika virus.”

For scientists studying the inner workings of cells, it is often essential to understand what individual molecules look like. The shape of a protein, for example, can determine its function. However, before cryo-electron microscopy became widely available roughly five years ago, scientists struggled to produce images of these molecules that weren’t blurry.

This book is dedicated to all noble-hearted individuals around the world who, with integrity and perseverance, pursue their studies, work, and projects in the service of justice, human dignity, and intellectual excellence. In particular, I honor my teachers in Peru: Haroli Alessandra Hernández Varilla, Federico Fernández Adriano, Luis Amparo Grimaldo Zapata, Enrique Verástegui Peláez, César Chambergo Rojas, Víctor Raúl Yactayo Castillo, Alessandra Martínez Tito, Ítala Preguntegui Garrafa, José Campos, Carlos Vera Scamarone, Diego Alberto Silberg, Domingo Jesús Mavila Salón, Teresa Esperanza Zamudio Ojeda, Richard Lovera Herrera, José Antonio Ñique de la Puente—"El Niño de Praga"—Santos Vera Sandoval, affectionately known as "REY de Lambayeque," José Campos Saavedra, Orlando Vera de la Cruz, Mario Miranda Morales, Maritza Romero, Jesús Ruiz, Brigite Aguirre Obando, Rubí Pariona Claux, Sol de los Ángeles Gómez, Milagros del Rosario Ravello Fernández, Fabián Fernández de la Cruz (and sister), their nephews and family members of goodwill, and Jesús Rojas—a universal promise in the humanities.

I also pay tribute to my legendary mentors from across the globe, including Martha Minow, Adolfo Pérez Esquivel, Glenn Cohen, Daniel Chapán, Miguel Ángel Ciuro Caldani, ...

PRÓLOGO.
Desde tiempos inmemoriales, la humanidad ha buscado descifrar los misterios del universo, expandiendo los límites del conocimiento con cada innovación y avance científico. En la era de la computación cuántica, nos encontramos en un umbral donde la velocidad del procesamiento de datos no solo redefine la tecnología, sino que también plantea profundas cuestiones éticas y filosóficas sobre el destino de nuestra especie.
Este trabajo colaborativo se sumerge en la intersección entre la innovación científica y la reflexión humanista, explorando cómo la computación cuántica, en su inexorable búsqueda por optimizar sistemas, acelerar descubrimientos y transformar industrias, debe también responder a una pregunta fundamental: ¿hacia qué destino dirigimos su inmenso poder?
A través de un análisis que abarca desde la física cuántica hasta la filosofía moral, esta obra invita a la comunidad académica, empresarial y científica a cuestionar el papel de la tecnología en la construcción de un futuro más equitativo, justo y sostenible. Porque el progreso, para ser verdaderamente significativo, debe estar guiado no solo por la eficiencia y la velocidad, sino también por la sabiduría y la responsabilidad
ucional:
https://www.nobelprize.org/prizes/chemistry/2023/brus/facts/
https://www.chem.columbia.edu/content/louis-e-brus

With the increase of the search for computational models where the expression of parallelism occurs naturally, some paradigms arise as options for the next generation of computers. In this context, dynamic Dataflow and Gamma-General Abstract Model for Multiset mAnipulation)-emerge as interesting computational models choices. In the dynamic Dataflow model, operations are performed as soon as their associated operators are available, without rely on a Program Counter to dictate the execution order of instructions. The Gamma paradigm is based on a parallel multiset rewriting scheme. It provides a non-deterministic execution model inspired by an abstract chemical machine metaphor, where operations are formulated as reactions that occur freely among matching elements belonging to the multiset. In this work, equivalence relations between the dynamic Dataflow and Gamma paradigms are exposed and explored, while methods to convert from Dataflow to Gamma paradigm and vice versa are provided. It is shown that vertices and edges of a dynamic Dataflow graph can correspond, respectively, to reactions and multiset elements in the Gamma paradigm. Implementation aspects of execution environments that could be mutually beneficial to both models are also discussed. This work provides the scientific community with the possibility of taking profit of both parallel programming models, contributing with a versatility component to researchers and developers. Finally, it is important to state that, to the best of our knowledge, the similarity relations between both dynamic Dataflow and Gamma models presented here have not been reported in any previous work.

This paper addresses the definition of the requirements for the design of a neural network associative memory, with on-chip training, in standard digital CMOS technology. We investigate various learning rules which are integrable in silicon, and we study the associative memory properties of the resulting networks. We also investigate the relationships between the architecture of the circuit and the learning rule, in order to minimize the extra circuitry required for the implementation of training. We describe a 64neuron associative memory with on-chip training, which has been manufactured, and we outline its future extensions. Beyond the application to the specific circuit described in the paper, the general methodology for determining the accuracy requirements can be applied to other circuits and to other auto-associative memory architectures.

This paper presents a simple and safe compiler, called MinSIGNAL, from a subset of the synchronous dataflow language SIGNAL to C, as well as its existing enhancements. The compiler follows a modular architecture, and can be seen as a sequence of source-to-source transformations applied to an intermediate representation which is named Synchronous Clocked Guarded Actions (S-CGA) and translation to sequential imperative code. Objective Caml (OCaml) is used for the implementation of MinSIGNAL. As a modern functional language, OCaml is adapted to symbolic computation and so, particularly suitable for compiler design and implementation of formal analysis tools. In particular, the safety of its type checking allows to skip some verification that would be mandatory with other languages. Additionally, this work is a basis for the formal verification of the compilation of SIGNAL with a theorem prover such as Coq.

This paper presents a simple and safe compiler, called MinSIGNAL, from a subset of the synchronous dataflow language SIGNAL to C, as well as its existing enhancements. The compiler follows a modular architecture, and can be seen as a sequence of source-to-source transformations applied to an intermediate representation which is named Synchronous Clocked Guarded Actions (S-CGA) and translation to sequential imperative code. Objective Caml (OCaml) is used for the implementation of MinSIGNAL. As a modern functional language, OCaml is adapted to symbolic computation and so, particularly suitable for compiler design and implementation of formal analysis tools. In particular, the safety of its type checking allows to skip some verification that would be mandatory with other languages. Additionally, this work is a basis for the formal verification of the compilation of SIGNAL with a theorem prover such as Coq.

The seminar emphasized four issues: o sta.tic program analysis o extensions for progra.mmer control of pa.ra.llelism o functional+logic languages and constraints o implementa.tion of functiona.l la.ngua.ges There were two formal discussion sessions, which a.ddressed the problems of input/output in functional la.nguages, and the utility of static program analysis. It is gratifying that the first Dagstuhl seminar in this area. (Functional Languages: Optimization for Parallelism) had stimulated many developments which were reported a.t this one. A particular feature of this seminar was the la.rge number of prototypes which were demonstrated a.nd which vividly illustrated the issues raised in discussions and presentations. Static Program Analysis Static program analysis has been thoroughly investigated for optimising sequential implementations, but parallel ones offer new problems. Discovering properties of synchronisation, for example, requires richer domains than those used in the sequential setting, lea.ding to a combinatorial explosion in cost. Current sequential analyses operate at or beyond the limits of today s algorithm technology. The most expensive aspect is computing xpoints, which requires a convergence test am] therefore a decision procedure for equality of abstract values. New work reported here helps reduce the need for convergence tests. and, using the algebraic properties of the operators in the resulting straightline code, partial evaluation ca.n be used to generate very efficient parallel code with a high degree of processor utilization. This is exempli ed by the well-known problem of parallel evaluation of expressions (and, more generally, straight-line code) over a semi-ring. In this context, partial evaluation is evaluation over the induced polynomial semi-ring that can be executed in parallel by tree contraction.

Kolam is a ritual art form practised by people in South India and consists of rule-bound geometric patterns of dots and lines. Single loop Kolams are mathematical closed loop patterns drawn over a grid of dots and conforming to certain heuristics. In this work, we propose a novel encoding scheme where we map the angular movements of Kolam at lattice points into sequences containing 4 distinct symbols. This is then used to simulate single loop Kolam procedure via turtle moves in accordance with the desired angular direction at specific points. We thus obtain sequential codes for Kolams, unique up to cyclic permutations. We specify the requirements for the algorithm and indicate the general methodology. We demonstrate a sample of Kolams using our algorithm with a software implementation in Python.

Kolam is a ritual art form practised by people in South India and consists of rule-bound geometric patterns of dots and lines. Single loop Kolams are mathematical closed loop patterns drawn over a grid of dots and conforming to certain heuristics. In this work, we propose a novel encoding scheme where we map the angular movements of Kolam at lattice points into sequences containing 4 distinct symbols. This is then used to simulate single loop Kolam procedure via turtle moves in accordance with the desired angular direction at specific points. We thus obtain sequential codes for Kolams, unique up to cyclic permutations. We specify the requirements for the algorithm and indicate the general methodology. We demonstrate a sample of Kolams using our algorithm with a software implementation in Python.

In this paper we first review the knowledge-based approach to software construction. The knowledge-based approach to software develoment promises attractive solutions to the problems plaguing software develoment. But to realize these benefits knowledge about programming must be well understood, formalized and engineered into a functioning system. This paper presents some aspects of such a formalization, describing knowledge about the procedural implementation of recursive functions and about data structure selection. A significant feature of this approach is that a single transformation may substantially affect the program form, implenienting a high-level design decision. A detailed derivation of a depth-first search algorithm to compute the height of a directed acyclic graph is presented.

Intensional logic is concerned with assertions and other expressions whose meaning depends on an implicit context. An intensional language is both a programming language and, at the same time, a formal system based on intensional semantics. It provides users with context-switching operators which allow values from different contexts to be combined without explicit context manipulation. Plane Lucid is an extension of the language Lucid, a language based on intensional logic. The language allows values of expressions in a program to vary in space as well as in time; it provides spatial and temporal operators to combine values from different contexts (or different points in space and time). As an application of Plane Lucid, an intensional 3-D spreadsheet has been designed in which Plane Lucid is the definition language of the spreadsheet. The spreadsheet is considered as a single entity (called the spreadsheet variable) which varies in spatial and temporal dimensions; values of cells in the spreadsheet are values of the spreadsheet variable at different spatial and temporal points.

In this paper we present a revised formulation and a correctness proof of Yaghi's [18] transformation algorithm from first-order extensional programs to intensional programs of nullary variables. The formal definition of the algorithm is a functional one, and its main difference from the one given in [18] is that if two expressions in the source program are identical, then they are assigned identical intensional expressions during the translation. The correctness proof of the algorithm is established by showing that a function call in the extensional program has-informally speaking-the same meaning as the intensional expression that results from its translation.

ASP is simpler since it requires just 12 species and 16 reactions as opposed to 14 species and 30 reactions required by WRP.  ASP employs the Runge-Kutta 4 numerical integration of the rate differential equations, which produces a higher-precision concentration series.  ASP learns by a more biologically plausible reinforcement method (penalty signal).  ASP determines the output value by thresholding as opposed to the comparison of positive and negative output species concentrations.  ASP is transformable to the DNA-strand displacement primitives by Soloveichik's method giving our symbolic species DNA-strand counterparts.

Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at data in the past would be a valuable tool to perform complex calculations. In this paper we present the first implementation of a chemical delay line providing information storage that can reliably capture information over an extended period of time. The delay line is capable of parallel operations in a single instruction, multiple data (SIMD) fashion. Using mass action and Michaelis-Menten kinetics, we describe the chemical delay line implementation featuring enzymes acting as a means to reduce copy errors. We also discuss how information is randomly accessible from any element on the delay line. Our work shows how the chemical delay line retains and provides a value from a previous cycle. The system's modularity allows for integration with existing chemical systems. We exemplify...

Current synthetic chemical systems lack the ability to selfmodify and learn to solve desired tasks. In this paper we introduce a new parallel model of a chemical delay line, which stores past concentrations over time with minimal latency. To enable temporal processing, we integrate the delay line with our previously proposed analog chemical perceptron. We show that we can successfully train our new memory-enabled chemical learner on four non-trivial temporal tasks: the linear moving weighted average, the moving maximum, and two variants of the Nonlinear AutoRegressive Moving Average (NARMA). Our implementation is based on chemical reaction networks and follows mass-action and Michaelis-Menten kinetics. We show that despite a simple design and limited resources, a single chemical perceptron extended with memory of variable size achieves 93-99% accuracy on the above tasks. Our results present an important step toward actual biochemical systems that can learn and adapt. Such systems have applications in biomedical diagnosis and smart drug delivery.

The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification based on external stimuli would be highly desirable. However, so far, it has been too challenging to implement these in wet chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports Michaelis-Menten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions and their simplicity allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt.

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and threshold. Both chemical perceptrons can successfully identify all 14 linearly separable two-input logic functions and maintain high robustness against rate-constant perturbations. We suggest that DNA strand displacement could, in principle, provide ...

Information about the existence of periodic patterns in a database workload can play a big part in the process of database tuning. However, full analysis of audit trails can be cumbersome and time-consuming. This paper discusses a heuristic algorithm that focuses on workload reconstruction based on pattern discovery in a simplified workload notation. This notation is based on multisets representing database actions (such as user queries) requiring access to specific persistent objects, but without the access cost analysis. Each action in this notation is a multiset of accessed objects, which can be tables, system files, views, etc. The theoretical model for such an approach has been discussed in detail in the authors' previous work [1] This work is mostly proof-of-a-concept for the theoretical approach. Additionally, in order to test the performance of the proposed algorithm, a testdata generator has been constructed. Both the previous and the current papers are parts of a research project dealing with the application of periodic pattern theory to the field of database optimization and tuning [2], [3], [4], [5].

We consider dataflow architecture for two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. We improve the earlier technique of almost continuous program transformations by adopting a discipline of bipartite graphs linking nodes obtained via general transformations and nodes obtained via linear transformations which makes it possible to develop and evolve dataflow programs over these classes of computations by continuous program transformations. The use of bipartite graphs allows us to represent the dataflow programs from this class as matrices of real numbers and evolve and modify programs by continuous change of these numbers. We develop a formalism for higher-order dataflow programming for this class of dataflow graphs based on the higher-order matrix elements. Some of our software experiments are briefly discussed.

The objective of this paper is to present a mathematically grounded description of the two topological spaces for the design problem and the design solution. These spaces are derived in a generalized form such that they can be applied by researchers studying engineering design and developing new methods or engineers seeking to understand the influence that changes in the problem space have on the solution space. In addition to the formal definitions of the spaces, including assumptions and limitations, three types of supported reasoning are presented to demonstrate the potential uses. These include similarity analysis to compare spaces, an approach to sensitivity analysis of the solution space to changes in the problem space, and finally a distance measure to determine how far a current proposal is to the feasible solution space. This paper is presented to establish a common vocabulary for researchers when discussing, studying, and supporting the dyadic nature of engineering design (problem-solution co-evolution).

In this position paper, we question the rationals behind the design of unconventional programming languages. Our questions are classified in four categories: the sources of inspiration for new computational models, the process of developing a program, the forms and the theories needed to write and understand nonclassical programs and finally the new computing media and the new applications that drive the development of new programming languages.

In this position paper, we question the rationale behind the design of unconventional programming languages. Our questions are classified in four categories: the sources of inspiration for new computational models, the process of developing a program, the forms and the theories needed to write and understand non-classical programs and finally the new computing media and the new applications that drive the development of new programming languages.

Sorting is one of the fundamental problems in computer science and being used vastly in various domains. So different serial and parallel approaches have been proposed. One of the parallel sorting methods are algorithms that are based on computational model of cellular automata. A cellular automata machine is a structure of interconnected elementary automata evolving in a parallel and synchronous way .The most famous sorting algorithm for one dimensional cellular automata machine is Gordillo and Luna's. This algorithm sorts n numbers in 2n-3 steps. In this paper three new sorting algorithms are proposed. In the two first proposed algorithms, despite using smaller neighborhood radius, sorting steps have not been changed and in the third algorithm sorting steps are reduced by regarding same neighborhood radius as Gordillo-Luna's second algorithms.

For realizing neural networks with binary memristor crossbars, memristors should be programmed by high-resistance state (HRS) and low-resistance state (LRS), according to the training algorithms like backpropagation. Unfortunately, it takes a very long time and consumes a large amount of power in training the memristor crossbar, because the program-verify scheme of memristor-programming is based on the incremental programming pulses, where many programming and verifying pulses are repeated until the target conductance. Thus, this reduces the programming time and power is very essential for energy-efficient and fast training of memristor networks. In this paper, we compared four different programming schemes, which are F-F, C-F, F-C, and C-C, respectively. C-C means both HRS and LRS are coarse-programmed. C-F has the coarse-programmed HRS and fine LRS, respectively. F-C is vice versa of C-F. In F-F, both HRS and LRS are fine-programmed. Comparing the error-energy products among the f...

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As a result, a wide range of technologies have been developed that, in turn, are described via distinct empirical models. This diversity of technologies requires the establishment of versatile tools that can enable designers to translate memristors’ attributes in novel neuro-inspired topologies. In this study, we present NeuroPack, a modular, algorithm-level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. The NeuroPack environment is designed with versatility being central, allowing the user to choose from a variety of neuron models, learning rules, and memristor models. Its hierarchical structure empowers NeuroPack to predict any memristor state changes and the corresponding neur...

For realizing neural networks with binary memristor crossbars, memristors should be programmed by high-resistance state (HRS) and low-resistance state (LRS), according to the training algorithms like backpropagation. Unfortunately, it takes a very long time and consumes a large amount of power in training the memristor crossbar, because the program-verify scheme of memristor-programming is based on the incremental programming pulses, where many programming and verifying pulses are repeated until the target conductance. Thus, this reduces the programming time and power is very essential for energy-efficient and fast training of memristor networks. In this paper, we compared four different programming schemes, which are F-F, C-F, F-C, and C-C, respectively. C-C means both HRS and LRS are coarse-programmed. C-F has the coarse-programmed HRS and fine LRS, respectively. F-C is vice versa of C-F. In F-F, both HRS and LRS are fine-programmed. Comparing the error-energy products among the f...

Cellular automata have proved many of its capabilities and have bestowed a lot in many fields. With the emergence of CA, fabric pattern production has increased in less amount of time. For weaving, cellular automata start with some pattern, then continues with a sequence of steps to produce a new pattern or to change the colour of the cell in a lattice, by using particular transition rules. The main purpose of using cellular automata algorithm as it provides the superlative edge maps and the outstanding quality with one pixel wide edge, with edges having no breaks. We assessed cellular automata capabilities and CA a lot contributions in pattern generation, transformation, and data processing. This research paper reflects the integration of cellular automata across different disciplines. In this paper, we assess the computationally enriched CA rules facilitating spot detection in the study of medical images for cancer diagnosis. The dynamic behaviour of CA increases the scope of transformation and makes it practical for morphing. CA provides prediction of protein structural class and processes dynamic simulation of protein. Flexibility of CA facilitates parallel processing using VLSI, colour graph modelling the linear rules, and its exercise in fabric weaving.

Memristor-based crossbars are considered to be promising candidates to accelerate vector-matrix computation in deep neural networks. Before being applied for inference, mem-ristors in the crossbars should be programmed to conductances corresponding to the network weights after software training. Existing programming methods, however, adjust conductances of memristors individually with many programming-reading cycles. In this paper, we propose an efficient programming framework for memristor crossbars, where the programming process is partitioned into the predictive phase and the fine-tuning phase. In the predictive phase, multiple memristors are programmed simultaneously with a memristor programming model and IR-drop estimation. To deal with the programming inaccuracy resulting from process variations, noise and IR-drop and move conductances to target values, memristors are fine-tuned afterwards to reach a specified programming accuracy. Simulation results demonstrate that the propo...

Memristors are emerging as powerful nanoscale devices for diverse applications, such as high-density memories and neuromorphic applications. However, this nascent technology requires considerable advancement before this vision is realized. We present a highly configurable CMOS interface chip which enables the characterization of on-chip memristors, especially for memory applications. The chip was fabricated in On-Semi 3M2P 0.5 µm occupying 2×2 mm 2. The chip design allows for post-CMOS fabrication of memristors. The interface between the memristor and the CMOS circuitry was provided via a top metal contact. The chip was designed to support an areadistributed interface decoupling CMOS pitch and memristor pitch, enabling high-density memristor integration. Measurement results on post-CMOS fabricated Ag/SiO 2 /Pt memristive devices are reported. Though we have shown the results from one memristive material stack, thorough chip characterization demonstrates the versatility of the chip enabling its use with a wide variety of materials stacks.

Memristors have shown promising features for enhancing neuromorphic computing concepts and AI hardware accelerators. In this paper, we present a user-friendly software infrastructure that allows emulating a wide range of neuromorphic architectures with memristor models. This tool empowers studies that exploit memristors for online learning and online classification tasks, predicting memristor resistive state changes during the training process. The versatility of the tool is showcased through the capability for users to customise parameters in the employed memristor and neuronal models as well as the employed learning rules. This further allows users to validate concepts and their sensitivity across a wide range of parameters. We demonstrate the use of the tool via an MNIST classification task. Finally, we show how this tool can also be used to emulate the concepts under study in-silico with practical memristive devices via appropriate interfacing with commercially available characterisation tools.

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As a result, a wide range of technologies have been developed that, in turn, are described via distinct empirical models. This diversity of technologies requires the establishment of versatile tools that can enable designers to translate memristors’ attributes in novel neuro-inspired topologies. In this study, we present NeuroPack, a modular, algorithm-level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. The NeuroPack environment is designed with versatility being central, allowing the user to choose from a variety of neuron models, learning rules, and memristor models. Its hierarchical structure empowers NeuroPack to predict any memristor state changes and the corresponding neur...

We present the design and implementation of a generative geometric kernel 1. The kernel generator is generic, type-safe, parametrized by many design-level choices and extensible. The resulting code has minimal traces of the design abstractions. We achieve genericity through a layered design deriving concepts from affine geometry, linear algebra and abstract algebra. We achieve parametrization and type-safety by using OCaml's module system, including higher order modules. The cost of abstraction is removed by using MetaO-Caml's support for code generation coupled with some annotations atop the code type.

With an increasing demand for artificial intelligence, the emulation of the human brain in neuromorphic computing has led to an extraordinary result in not only simulating synaptic dynamics but also reducing complex circuitry systems and algorithms. In this work, an artificial electronic synaptic device based on a synthesized MoS2 memristor array (4 × 4) is demonstrated; the device can emulate synaptic behavior with the simulation of deep neural network (DNN) learning. MoS2 film is directly synthesized onto a patterned bottom electrode (Pt) with high crystallinity using sputtering and CVD. The proposed MoS2 memristor exhibits excellent memory operations in terms of endurance (up to 500 sweep cycles) and retention (~ 104) with a highly uniform memory performance of crossbar array (4 × 4) up to 16 memristors on a scalable level. Next, the proposed MoS2 memristor is utilized as a synaptic device that demonstrates close linear and clear synaptic functions in terms of potentiation and de...

The main motivation of 81/2 is to develop a high-level language that supports the parallel simulation of dynamical processes [1, 2]. To achieve this goal, a new data-structure, that merges the concept of stream and collection is introduced in a declarative framework. After a brief description of 81/2 basics, we describe the introduction of dynamicity and symbolic values in the language. We focus on the expressivity and issues brought by the new dynamic possibilities of the language and show, through several paradigmatic examples, that our computation model is able to support parallel symbolic processing. * This research is partially supported by the operation "Programmation parall~le et dis-tribu~e" of the french "GDR de programmation".

We present the rst results in the development of a new declarative programming language called MGS. This language is devoted to the simulation of biological processes, especially those whose state space must be computed jointly with the running state of the system (for instance, in morphogenesis). MGS proposes a uni ed view on several computational mechanisms. Some of them are initially inspired by biological or chemical processes (Gamma and the CHAM, Lindenmayer systems, Paun systems and cellular ...

Bio-molecular computing, 'computations performed by bio-molecules', is already challenging traditional approaches to computation both theoretically and technologically. Often placed within the wider context of ´bio-inspired` or 'natural' or even 'unconventional' computing, the study of natural and artificial molecular computations is adding to our understanding of biology, physical sciences and computer science well beyond the framework of existing design and implementation paradigms. In this introduction, We wish to outline the current scope of the field and assemble some basic arguments that, bio-molecular computation is of central importance to computer science, physical sciences and biology using HOL-Higher Order Logic. HOL is used as the computational tool in our R&D work. DNA was analyzed as a chemical computing engine, in our effort to develop novel formalisms to understand the molecular scale biochemical computing behavior using HOL. In our view, our focus is one of the pioneering efforts in this promising domain of nano-bio scale chemical information processing dynamics.

In this short paper one overviews the two years development of kernel P systems (kP systems for short), a basic class of P systems combining features of different variants of such systems. The definition of kP systems is given, some examples illustrate various features of the model and the most significant results are presented.

In this position paper, we question the rationale behind the design of unconventional programming languages. Our questions are classified in four categories: the sources of inspiration for new computational models, the process of developing a program, the forms and the theories needed to write and understand non-classical programs and finally the new computing media and the new applications that drive the development of new programming languages.

The sensing capabilities of zinc oxide nano/micro-structures have been widely investigated and these structures are frequently used in the fabrication of cutting-edge sensors. However, to date, little attention has been paid to the multi-sensing abilities of this material. In this work, we present an efficient multisensor based on a single zinc oxide microwire/gold junction. The device is able to detect in real time three different stimuli, UV-VIS light, temperature and pH variations. This is thanks to three properties of zinc oxide its photoconductive response, pyroelectricity and surface functionalization with amino-propyl groups, respectively. The three stimuli can be detected either simultaneously or in a sequence/random order. A specific mathematical tool was also developed, together with a design of experiments (DoE), to predict the performances of the sensor. Our micro-device allows reliable and versatile real-time measurements of UV-VIS light, temperature and pH variations. Therefore, it shows great potential for use in the field of sensing for living cell cultures.

Neuromorphic hardware computing is a promising alternative to von Neumann computing by virtue of its parallel computation, and low power consumption. To implement neuromorphic hardware based on deep neural network (DNN), a number of synaptic devices should be interconnected with neuron devices. For ideal hardware DNN, not only scalability and low power consumption, but also a linear and symmetric conductance change with the large number of conductance levels are required. Here an all-solid-state polymer electrolyte-gated synaptic transistor (pEGST) was fabricated on an entire silicon wafer with CMOS microfabrication and initiated chemical vapor deposition (iCVD) process. The pEGST showed good linearity as well as symmetry in potentiation and depression, conductance levels up to 8,192, and low switching energy smaller than 20 fJ/pulse. Selected 128 levels from 8,192 used to identify handwritten digits in the MNIST database with the aid of a multilayer perceptron, resulting in a recognition rate of 91.7 %.

Engineering distributed applications and services in emerging and open computing scenarios like the Internet of Things, cyberphysical systems and pervasive computing, calls for identifying proper abstractions to smoothly capture collective behaviour, adaptivity, and dynamic injection and execution of concurrent distributed activities. Accordingly, we introduce a notion of "aggregate process" as a concurrent field computation whose execution and interactions are sustained by a dynamic team of devices, and whose spatial region can opportunistically vary over time. We formalise this notion by extending the Field Calculus with a new primitive construct, spawn, used to instantiate a set of field computations and regulate key aspects of their life-cycle. By virtue of an open-source implementation in the ScaFi framework, we show basic programming examples and benefits via two case studies of mobile ad-hoc networks and drone swarm scenarios, evaluated by simulation.

Neuromorphic hardware computing is a promising alternative to von Neumann computing by virtue of its parallel computation, and low power consumption. To implement neuromorphic hardware based on deep neural network (DNN), a number of synaptic devices should be interconnected with neuron devices. For ideal hardware DNN, not only scalability and low power consumption, but also a linear and symmetric conductance change with the large number of conductance levels are required. Here an all-solid-state polymer electrolyte-gated synaptic transistor (pEGST) was fabricated on an entire silicon wafer with CMOS microfabrication and initiated chemical vapor deposition (iCVD) process. The pEGST showed good linearity as well as symmetry in potentiation and depression, conductance levels up to 8,192, and low switching energy smaller than 20 fJ/pulse. Selected 128 levels from 8,192 used to identify handwritten digits in the MNIST database with the aid of a multilayer perceptron, resulting in a recognition rate of 91.7 %.

We argue that classical programming languages are based on a fundamentally mistaken emphasis on the operational aspect of computation. These languages are seen as the means by which the programmer brings about particular kinds of operational activity (such as procedure calling or message passing). We suggest an alternate philosophy which places the emphasis on static, extensional (mathematical) concepts such as that of function or sequence. We outline a simple functional language (Luswim) based on these principles. The Luswim programmer specifies the desired output, and operational concepts such as dataflow, message passing or coroutine linkage can be used to 'evaluate' the specification. Operational activity occupies its proper place, namely as means to an end rather than an end in itself.