STAP: An Architecture and Design Tool for Automata Processing on Memristor TCAMs

Accelerating finite-state automata benefits several emerging application domains that are built on pattern matching. In-memory architectures, such as the Automata Processor (AP), are efficient to speed them up, at least for outperforming traditional von-Neumann architectures. In spite of the AP’s massive parallelism, current APs suffer from poor memory density, inefficient routing architectures, and limited capabilities. Although these limitations can be lessened by emerging memory technologies, its architecture is still the major source of huge communication demands and lack of scalability. To address these issues, we present STAP , a Scalable TCAM-based architecture for Automata Processing . STAP adopts a reconfigurable array of processing elements, which are based on memristive Ternary CAMs (TCAMs), to efficiently implement Non-deterministic finite automata (NFAs) through proper encoding and mapping methods. The CAD tool for STAP integrates the design flow of automata applications, a specific mapping algorithm, and place and route tools for connecting processing elements by RRAM-based programmable interconnects. Results showed 1.47× higher throughput when processing 16-bit input symbols, and improvements of 3.9× and 25× on state and routing densities over the state-of-the-art AP, while preserving 10 4 programming cycles.

Context-bounded verification of thread pools

Thread pooling is a common programming idiom in which a fixed set of worker threads are maintained to execute tasks concurrently. The workers repeatedly pick tasks and execute them to completion. Each task is sequential, with possibly recursive code, and tasks communicate over shared memory. Executing a task can lead to more new tasks being spawned. We consider the safety verification problem for thread-pooled programs. We parameterize the problem with two parameters: the size of the thread pool as well as the number of context switches for each task. The size of the thread pool determines the number of workers running concurrently. The number of context switches determines how many times a worker can be swapped out while executing a single task---like many verification problems for multithreaded recursive programs, the context bounding is important for decidability. We show that the safety verification problem for thread-pooled, context-bounded, Boolean programs is EXPSPACE-complete, even if the size of the thread pool and the context bound are given in binary. Our main result, the EXPSPACE upper bound, is derived using a sequence of new succinct encoding techniques of independent language-theoretic interest. In particular, we show a polynomial-time construction of downward closures of languages accepted by succinct pushdown automata as doubly succinct nondeterministic finite automata. While there are explicit doubly exponential lower bounds on the size of nondeterministic finite automata accepting the downward closure, our result shows these automata can be compressed. We show that thread pooling significantly reduces computational power: in contrast, if only the context bound is provided in binary, but there is no thread pooling, the safety verification problem becomes 3EXPSPACE-complete. Given the high complexity lower bounds of related problems involving binary parameters, the relatively low complexity of safety verification with thread-pooling comes as a surprise.

Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited

Reservoir computers (RCs) and recurrent neural networks (RNNs) can mimic any finite-state automaton in theory, and some workers demonstrated that this can hold in practice. We test the capability of generalized linear models, RCs, and Long Short-Term Memory (LSTM) RNN architectures to predict the stochastic processes generated by a large suite of probabilistic deterministic finite-state automata (PDFA) in the small-data limit according to two metrics: predictive accuracy and distance to a predictive rate-distortion curve. The latter provides a sense of whether or not the RNN is a lossy predictive feature extractor in the information-theoretic sense. PDFAs provide an excellent performance benchmark in that they can be systematically enumerated, the randomness and correlation structure of their generated processes are exactly known, and their optimal memory-limited predictors are easily computed. With less data than is needed to make a good prediction, LSTMs surprisingly lose at predictive accuracy, but win at lossy predictive feature extraction. These results highlight the utility of causal states in understanding the capabilities of RNNs to predict.

Porosity of the porous carbonate rocks in the Jingfengqiao–Baidiao area based on finite automata

This study is based on the processing of computed microtomography images of rock samples. In this study, a finite automation is constructed using the grey value, red-green-blue (RGB) value and Euler number of polarized images of carbonate rocks from the Jingfengqiao–Baidiao area. The finite automaton is used to perform black and white binary processing of the polarized images of the carbonate rocks. The porosity of the carbonate rock is calculated based on the black and white binarization processing results of the polarized images of the carbonate rocks. The obtained porosity is compared with the carbonate porosity obtained by use of the traditional carbonate research method. When the two porosities are close, the image processing threshold of the finite automata is considered to be credible. Based on the finite automata established using the image processing threshold, the black and white binary images of the polarized images of the carbonate rocks are used to establish a rock pore image using I mage J2X. The polarized images of the carbonate rocks are classified according to their RGB values using the finite automata for the porosity classification, and the obtained images are used as textures to paste onto a cube to construct a three-dimensional data model of the carbonate rocks. This study also uses 16S rDNA analysis to verify the formation mechanism of the carbonate pores in the Jingfengqiao–Baidiao area. The results of the 16S rDNA analysis show that the pores in the carbonate rocks in the Jingfengqiao–Baidiao area are closely related to microorganisms, represented by denitrifying bacteria.

Penerapan Finite State Automata Pada Vending Machine Parfum Laundry Pakaian

Abstrak: Parfum banyak digemari oleh berbagai kalangan, salah satunya adalah parfum laundry. Parfum laundry memiliki berbagai macam varian aroma, meliputi aroma buah, aroma bunga, aroma perpaduan buah dan bunga, dan aroma kayu-kayuan. Parfum laundry ini merupakan rangkaian akhir dalam proses laundry. Saat ini pelanggan menerima hasil laundry dengan aroma parfum yang dipilih secara random sesuai ketersediaan pihak laundry, hal ini menjadi masalah karena pelanggan tidak dapat memilih aroma parfum laundry sesuai dengan keinginan mereka. Dengan alasan tersebut maka diperlukan suatu rancangan sebuah Vending Machine (VM), rancangan VM ini menggunakan metode Finite State Automata (FSA) dengan jenis Non-Deterministic Finite Automata (NFA), metode tersebut digunakan karena dapat mengakomodir lebih dari satu kondisi dalam satu pilihan. Metode FSA jenis NFA dibuat dengan tahapan analisa proses bisnis, diagram state, desain VM dan pengujian. Hasil dari penelitian ini dengan adanya VM pemilihan parfum laundry pakaian, maka mempermudah pelanggan untuk dapat memilih parfum laundry sehingga pelanggan memperoleh hasil laundry dengan aroma parfum sesuai pilihan mereka. Kata kunci: finite state automata, parfum laundry, vending machine Abstract: Perfume is much favored by various circles, one of which is laundry perfume. Laundry perfume has a wide range of scent variants, including fruity scents, floral scents, fruity floral scents, and woody scents. This laundry perfume is the final line in the laundry process. Currently customers receive laundry results with perfume scents selected randomly according to the availability of the laundry party, this is a problem because customers cannot choose laundry perfume scents according to their wishes. For this reason, it is necessary to design a Vending Machine (VM), this VM design uses the Finite State Automata (FSA) method with the Non-Deterministic Finite Automata (NFA) type, this method is used because it can accommodate more than one condition in one choice. The FSA type of NFA method is made with the stages of business process analysis, state diagrams, VM design and testing. The results of this study with the VM in the selection of clothes laundry perfume, it makes it easier for customers to be able to choose laundry perfumes so that customers get laundry results with the perfume scent of their choice. Keywords: finite state automata, laundry perfume, vending machine

Two Extensions of Cover Automata

Deterministic Finite Cover Automata (DFCA) are compact representations of finite languages. Deterministic Finite Automata with “do not care” symbols and Multiple Entry Deterministic Finite Automata are both compact representations of regular languages. This paper studies the benefits of combining these representations to get even more compact representations of finite languages. DFCAs are extended by accepting either “do not care” symbols or considering multiple entry DFCAs. We study for each of the two models the existence of the minimization or simplification algorithms and their computational complexity, the state complexity of these representations compared with other representations of the same language, and the bounds for state complexity in case we perform a representation transformation. Minimization for both models proves to be NP-hard. A method is presented to transform minimization algorithms for deterministic automata into simplification algorithms applicable to these extended models. DFCAs with “do not care” symbols prove to have comparable state complexity as Nondeterministic Finite Cover Automata. Furthermore, for multiple entry DFCAs, we can have a tight estimate of the state complexity of the transformation into equivalent DFCA.

Robust finite automata in stochastic chemical reaction networks

Finite-state automata (FSA) are simple computational devices that can nevertheless illustrate interesting behaviours. We propose that FSA can be employed as control circuits for engineered stochastic biological and biomolecular systems. We present an implementation of FSA using counts of chemical species in the range of hundreds to thousands, which is relevant for the counts of many key molecules such as mRNAs in prokaryotic cells. The challenge here is to ensure a robust representation of the current state in the face of stochastic noise. We achieve this by using a multistable approximate majority algorithm to stabilize and store the current state of the system. Arbitrary finite state machines can thus be compiled into robust stochastic chemical automata. We present two variants: one that consumes its input signals to initiate state transitions and one that does not. We characterize the state change dynamics of these systems and demonstrate their application to solve the four-bit binary square root problem. Our work lays the foundation for the use of chemical automata as control circuits in bioengineered systems and biorobotics.