Many people think of analog computing as a historic dead-end in computing. In fact, nothing could be further from the truth as analog computing - together with quantum computing - has the potential to bring computing to new levels with respect to raw computational power and energy efficiency. The following paper explains the limits of digital computers, gives a quick introduction to analog computing in general, and shows a number of recent developments that will change the way we think about computers in the next few years.
There have been many attempts to implement neural networks in the analog circuit. Most of them had a lot of input terms, and most studies implemented neural networks in the analog circuit through a circuit simulation program called Spice to avoid the need to design chips at a high cost and implement circuits directly to input them. In this study, we will implement neural networks using a capacitor and diode and use microcontrollers (Arduino Mega 2560 R3 boards) to drive real-world models and analyze the results.
Mohammad B. Khreishah, Walter C. Daugherity, Laszlo B. Kish
In this paper, we propose a new method of applying the XOR and XNOR gates on exponentially large superpositions in Instantaneous Noise-Based Logic. These new gates are repeatable, and they can achieve an exponential speed up in computation with a polynomial requirement in hardware complexity.
Noushin Raeisi Kheirabadi, Alessandro Chiolerio, Andrew Adamatzky
Pavlovian reflex is an essential mechanism of nervous systems of living beings which allows them to learn. Liquid colloid computing devices offer a high degree of fault-tolerance, reconfigurability and plasticity. As a first step towards designing and prototyping colloidal neuromorphic computing systems we decided to evaluate if it is possible to implement Pavlovian reflexes. We equate an increase of a synaptic weight with decreased resistance. In laboratory experiments we demonstrated that it is possible to implement Pavlovian learning using just two volumes of colloid liquid.
We demonstrate a novel mesh of interferometers for programmable optical processors. Employing an efficient programming scheme, the proposed architecture improves energy efficiency by 83% maintaining the same computation accuracy for weight matrix changes at 2 kHz.
AbstractThe crystal structures of (Et4N)2C2S6, (Et4N)2C2S5, (Et4N)2CS4, (Et4N)HCS3, (Et4N)2CS3 ⋅ 2H2O and (Et4N)2CS4‐y ⋅ yH2O (y=0.69) were determined by single‐crystal X‐ray diffraction analysis at 150 K. For the first time, salts with the C2S52− and HCS3− anions could be synthesized.
Here it is proposed a three-dimensional plasmonic nonvolatile memory crossbar arrays that can ensure a dual-mode operation in electrical and optical domains. This can be realized through plasmonics that serves as a bridge between photonics and electronics as the metal electrode is part of the waveguide. The proposed arrangement is based on low-loss long-range dielectric-loaded surface plasmon polariton waveguide where a metal stripe is placed between a buffer layer and ridge. To achieve a dual-mode operation the materials were defined that can provide both electrical and optical modulation functionality.
SIMD||DNA is a model of DNA strand displacement allowing parallel in-memory computation on DNA storage. We show how to simulate an arbitrary 3-symbol space-bounded Turing machine with a SIMD||DNA program, giving a more direct and efficient route to general-purpose information manipulation on DNA storage than the Rule 110 simulation of [Wang, Chalk, Soloveichik, DNA 2019]. We also develop software that can simulate SIMD||DNA programs and produce SVG figures.
Liquid electronic sensors are typically based on regular arrays of channels filled with a conductive liquid. We propose that a random planar network of conducting liquid allows us for a wider spectrum of electrical responses and localisation of tactile stimuli. We also speculate that a computation protocol can be implemented on such a network, featuring mechanical inputs and electrical outputs. Our results pave a way towards future developments on sensing and computing wearables with disordered sensing networks structure.
Digital microfluidic biochips (DMFBs) constitute modern generation of Lab-on-Chip (LoC) devices aimed at automation, miniaturization and cost-affordability of biochemistry and laboratory procedures. Over the course of past few years there have been various application-specific and general-purpose DMFBs aimed at reduced manufacturing costs; following the same trend this study presents a general-purpose DMFB with highly competitive characteristics compared with the state-of-the-art DMFBs. The proposed DMFB architecture provides lower Layout / PCB fabrication costs thereby reducing the total manufacturing costs. While more cost-affordable the proposed design is competitive with the state-of-the-art DMFB architectures.
Unary representation is straightforward, error tolerant and requires simple logic while its latency is a concern. On the other hand, positional representation (like binary) is compact and requires less space, but it is sensitive to errors. A hybrid representation called unary positional encoding reduces the latency of unary computation and length of the encoded stream, thus achieves the compactness of positional representation while preserving the error tolerance of unary encoding. In this paper, we discuss the prospect of unary positional encoding in spiking neural systems by incorporating temporal and rate encoding.
The class of problems represented by frustrated cluster loops, FCL, is a robust set of problems that spans a wide range of computational difficulty and that are easy to determine what their solutions are. Here, we use frustrated cluster loops to test the relative performance of the D-Wave without post-processing and the D-Wave with multi-qubit correction (MQC) post-processing. MQC post-processing has shown itself exceptionally beneficial in improving the performance of the D-Wave 2000Q when processing difficult FCL problems.
For more than 45 years, many multi-valued circuits have been presented. With very rare exceptions, they have been unsuccessful for fundamental reasons that can be explained. Each time a new circuit technology is presented, a lot of new MVL circuits are proposed. Can new circuit technologies overcome the fundamental disadvantages of MVL circuits? The evolution of IC technologies in the last decades unfortunately increases the disadvantage of MVL circuits versus binary ones. For non conventional technologies, only quantum devices look promising, even if implementation is challenging and applications are restricted to a small niche.
In this paper, reversible circuits consisting of NOT, CNOT and 2-CNOT gates are studied. Several asymptotically optimal by the order of magnitude synthesis methods are described. Some circuit's complexity reduction approaches are considered. Implementation of discrete logarithm within a reversible circuit is discussed. The main conclusion in the paper is that the usage of additional inputs (additional memory) in reversible circuits almost always allow to reduce their complexity, depth and weight.
We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.
In this paper, we report efficient quantum circuits for integer multiplication using Toom-Cook algorithm. By analysing the recursive tree structure of the algorithm, we obtained a bound on the count of Toffoli gates and qubits. These bounds are further improved by employing reversible pebble games through uncomputing the intermediate results. The asymptotic bounds for different performance metrics of the proposed quantum circuit are superior to the prior implementations of multiplier circuits using schoolbook and Karatsuba algorithms.
Max Talanov, Evgenii Zykov, Yuriy Gerasimov
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In this technical report we present novel results of the dopamine neuromodulation inspired modulation of a polyaniline (PANI) memristive device excitatory learning STDP. Results presented in this work are of two experiments setup computer simulation and physical prototype experiments. We present physical prototype of inhibitory learning or iSTDP as well as the results of iSTDP learning.