• arXiv.cs.ET Pub Date : 2020-12-01
Abhiroop Bhattacharjee; Lakshya Bhatnagar; Youngeun Kim; Priyadarshini Panda

Memristive crossbars suffer from non-idealities (such as, sneak paths) that degrade computational accuracy of the Deep Neural Networks (DNNs) mapped onto them. A 1T-1R synapse, adding a transistor (1T) in series with the memristive synapse (1R), has been proposed to mitigate such non-idealities. We observe that the non-linear characteristics of the transistor affect the overall conductance of the 1T-1R

更新日期：2020-12-02
• arXiv.cs.ET Pub Date : 2020-11-30
M. Ezzadeen; D. Bosch; B. Giraud; S. Barraud; J. -P. Noel; D. Lattard; J. Lacord; J. -M. Portal; F. Andrieu

The Von-Neumann bottleneck is a clear limitation for data-intensive applications, bringing in-memory computing (IMC) solutions to the fore. Since large data sets are usually stored in nonvolatile memory (NVM), various solutions have been proposed based on emerging memories, such as OxRAM, that rely mainly on area hungry, one transistor (1T) one OxRAM (1R) bit-cell. To tackle this area issue, while

更新日期：2020-12-02
• arXiv.cs.ET Pub Date : 2020-11-30
Shihao Song; Anup Das; Onur Mutlu; Nagarajan Kandasamy

Modern computing systems are embracing non-volatile memory (NVM) to implement high-capacity and low-cost main memory. Elevated operating voltages of NVM accelerate the aging of CMOS transistors in the peripheral circuitry of each memory bank. Aggressive device scaling increases power density and temperature, which further accelerates aging, challenging the reliable operation of NVM-based main memory

更新日期：2020-12-02
• arXiv.cs.ET Pub Date : 2020-11-30

Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of weights onto crossbars such that the power consumed by the crossbars and the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized.

更新日期：2020-12-01
• arXiv.cs.ET Pub Date : 2020-11-29
Susan M. Mniszewski; Pavel A. Dub; Sergei Tretiak; Petr M. Anisimov; Yu Zhang; Christian F. A. Negre

Calculating the ground state energy of a molecule efficiently is of great interest in quantum chemistry. The exact numerical solution of the electronic Schrodinger equation remains unfeasible for most molecules requiring approximate methods at best. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix

更新日期：2020-12-01
• arXiv.cs.ET Pub Date : 2020-11-10
Taqwa Saeed; Sergi Abadal; Christos Liaskos; Andreas Pitsillides; Hamidreza Taghvaee; Albert Cabellos-Aparicio; Marios Lestas; Eduard Alarcon

Metasurfaces are envisaged to play a key role in next-generation wireless systems due to their powerful control over electromagnetic waves. The last decade has witnessed huge advances in this regard, shifting from static to programmable metasurfaces. The HyperSurface (HSF) paradigm takes one step further by integrating a network of controllers within the device with the aim of adding intelligence,

更新日期：2020-12-01
• arXiv.cs.ET Pub Date : 2020-11-28
Gregory W. Wimsatt; Alexander B. Boyd; Paul M. Riechers; James P. Crutchfield

Nonequilibrium information thermodynamics determines the minimum energy dissipation to reliably erase memory under time-symmetric control protocols. We demonstrate that its bounds are tight and so show that the costs overwhelm those implied by Landauer's energy bound on information erasure. Moreover, in the limit of perfect computation, the costs diverge. The conclusion is that time-asymmetric protocols

更新日期：2020-12-01
• arXiv.cs.ET Pub Date : 2020-11-25
Mehmet Sukru Kuran; H. Birkan Yilmaz; Ilker Demirkol; Nariman Farsad; Andrea Goldsmith

This survey paper focuses on modulation aspects of molecular communication, an emerging field focused on building biologically-inspired systems that embed data within chemical signals. The primary challenges in designing these systems are how to encode and modulate information onto chemical signals, and how to design a receiver that can detect and decode the information from the corrupted chemical

更新日期：2020-12-01
• arXiv.cs.ET Pub Date : 2020-11-24
Herbert Jaeger

The acceleration race of digital computing technologies seems to be steering toward impasses -- technological, economical and environmental -- a condition that has spurred research efforts in alternative, "neuromorphic" (brain-like) computing technologies. Furthermore, since decades the idea of exploiting nonlinear physical phenomena "directly" for non-digital computing has been explored under names

更新日期：2020-11-25
• arXiv.cs.ET Pub Date : 2020-11-10
Steven Kisseleff; Symeon Chatzinotas; Björn Ottersten

Reconfigurable intelligent surfaces (RISs) have been introduced to improve the signal propagation characteristics by focusing the signal power in the preferred direction, thus making the communication environment 'smart'. The typical use cases and applications for the 'smart' environment include beyond 5G communication networks, smart cities, etc. The main advantage of employing RISs in such networks

更新日期：2020-11-25
• arXiv.cs.ET Pub Date : 2020-11-23
Lizhou Wu; Siddharth Rao; Mottaqiallah Taouil; Erik Jan Marinissen; Gouri Sankar Kar; Said Hamdioui

As a unique mechanism for MRAMs, magnetic coupling needs to be accounted for when designing memory arrays. This paper models both intra- and inter-cell magnetic coupling analytically for STT-MRAMs and investigates their impact on the write performance and retention of MTJ devices, which are the data-storing elements of STT-MRAMs. We present magnetic measurement data of MTJ devices with diameters ranging

更新日期：2020-11-25
• arXiv.cs.ET Pub Date : 2020-11-21
Melika Payvand; Mohammed E. Fouda; Fadi Kurdahi; Ahmed M. Eltawil; Emre O. Neftci

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm

更新日期：2020-11-25
• arXiv.cs.ET Pub Date : 2020-11-21
Hiroyasu Ando; T. Okamoto; H. Chang; T. Noguchi; Shinji Nakaoka

Owing to recent advances in artificial intelligence and internet of things (IoT) technologies, collected big data facilitates high computational performance, while its computational resources and energy cost are large. Moreover, data are often collected but not used. To solve these problems, we propose a framework for a computational model that follows a natural computational system, such as the human

更新日期：2020-11-25
• arXiv.cs.ET Pub Date : 2020-11-20
David Haley; David Doty

The thermodynamic binding networks (TBN) model was recently developed as a tool for studying engineered molecular systems. The TBN model allows one to reason about their behavior through a simplified abstraction that ignores details about molecular composition, focusing on two key determinants of a system's energetics common to any chemical substrate: how many molecular bonds are formed, and how many

更新日期：2020-11-25
• arXiv.cs.ET Pub Date : 2020-11-19
Ibrahim Al-Nahhal; Octavia A. Dobre; Ertugrul Basar

Reconfigurable intelligent surface-empowered communication (RIS) and sparse code multiple access (SCMA) are promising candidates for future generations of wireless networks. The former enhances the transmission environments, whereas the latter provides a high spectral efficiency transmission. This letter proposes, for the first time, an RIS-assisted uplink SCMA (SCMA-RIS) scheme to improve the conventional

更新日期：2020-11-21
• arXiv.cs.ET Pub Date : 2020-11-18
Zixin Huang; Siddarth Koduru Joshi; Djeylan Aktas; Cosmo Lupo; Armanda O. Quintavalle; Natarajan Venkatachalam; Sören Wengerowsky; Martin Lončarić; Sebastian Philipp Neumann; Bo Liu; Željko Samec; Laurent Kling; Mario Stipčević; Rupert Ursin; John G. Rarity

Anonymity in networked communication is vital for many privacy-preserving tasks. Secure key distribution alone is insufficient for high-security communications, often knowing who transmits a message to whom and when must also be kept hidden from an adversary. Here we experimentally demonstrate 5 information-theoretically secure anonymity protocols on an 8 user city-wide quantum network using polarisation-entangled

更新日期：2020-11-21
• arXiv.cs.ET Pub Date : 2020-11-18
Elisabetta Corti; Joaquin Antonio Cornejo Jimenez; Kham M. Niang; John Robertson; Kirsten E. Moselund; Bernd Gotsmann; Adrian M. Ionescu; Siegfried Karg

In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area density and oscillation frequency. Further, the crossbar devices exhibit low variability and extended reliability, hence, enabling experiments on 4-coupled

更新日期：2020-11-19
• arXiv.cs.ET Pub Date : 2020-11-18
M. Ali Vosoughi

The oscillator-based Ising machine (OIM) is a network of coupled CMOS oscillators that solves combinatorial optimization problems. In this paper, the distribution of the injection-locking oscillations throughout the circuit is proposed to accelerate the phase-locking of the OIM. The implications of the proposed technique theoretically investigated and verified by extensive simulations in EDA tools

更新日期：2020-11-19
• arXiv.cs.ET Pub Date : 2020-11-16
János Végh

Today's computing is told to be based on the classic paradigm, proposed by von Neumann, a three-quarter century ago. However, that paradigm was justified (for the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!) and led to catastrophic performance losses in computing systems, from operating gate level to large networks, including

更新日期：2020-11-18
• arXiv.cs.ET Pub Date : 2020-11-14
Shurui Li; Mario Miscuglio; Volker J. Sorger; Puneet Gupta

Low latency, high throughput inference on Convolution Neural Networks (CNNs) remains a challenge, especially for applications requiring large input or large kernel sizes. 4F optics provides a solution to accelerate CNNs by converting convolutions into Fourier-domain point-wise multiplications that are computationally 'free' in optical domain. However, existing 4F CNN systems suffer from the all-positive

更新日期：2020-11-17
• arXiv.cs.ET Pub Date : 2020-11-14
Juan Pablo Carbajal; Daniel Alejandro Martin; Dante Renato Chialvo

Recent results in adaptive matter revived the interest in the implementation of novel devices able to perform brain-like operations. Here we introduce a training algorithm for a memristor network which is inspired in previous work on biological learning. Robust results are obtained from computer simulations of a network of voltage controlled memristive devices. Its implementation in hardware is straightforward

更新日期：2020-11-17
• arXiv.cs.ET Pub Date : 2020-11-16
Tiago M. L. de Veras; Ismael C. S. de Araujo; Daniel K. Park; Adenilton J. da Silva

Loading data in a quantum device is required in several quantum computing applications. Without an efficient loading procedure, the cost to initialize the algorithms can dominate the overall computational cost. A circuit-based quantum random access memory named FF-QRAM can load M n-bit patterns with computational cost O(CMn) to load continuous data where C depends on the data distribution. In this

更新日期：2020-11-17
• arXiv.cs.ET Pub Date : 2020-11-13
Arman Kazemi; Mohammad Mehdi Sharifi; Ann Franchesca Laguna; Franz Müller; Ramin Rajaei; Ricardo Olivo; Thomas Kämpfe; Michael Niemier; X. Sharon Hu

Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support for accurate NN search is highly desirable. Ternary content-addressable memories (TCAMs) have been proposed to accelerate NN search for few-shot learning tasks by implementing $L_\infty$ and Hamming distance metrics, but they

更新日期：2020-11-17
• arXiv.cs.ET Pub Date : 2020-11-14
Lukas Burgholzer; Richard Kueng; Robert Wille

Verification of quantum circuits is essential for guaranteeing correctness of quantum algorithms and/or quantum descriptions across various levels of abstraction. In this work, we show that there are promising ways to check the correctness of quantum circuits using simulative verification and random stimuli. To this end, we investigate how to properly generate stimuli for efficiently checking the correctness

更新日期：2020-11-17
• arXiv.cs.ET Pub Date : 2020-11-13
Xavier Bonnetain; Samuel Jaques

We present the first complete implementation of the offline Simon's algorithm, and estimate its cost to attack the MAC Chaskey, the block cipher PRINCE and the NIST lightweight candidate AEAD scheme Elephant. These attacks require a reasonable amount of qubits, comparable to the number of qubits required to break RSA-2048. They are faster than other collision algorithms, and the attacks against PRINCE

更新日期：2020-11-16
• arXiv.cs.ET Pub Date : 2020-11-12
S. R. B. Bearden; Y. R. Pei; M. Di Ventra

Boolean satisfiability is a propositional logic problem of interest in multiple fields, e.g., physics, mathematics, and computer science. Beyond a field of research, instances of the SAT problem, as it is known, require efficient solution methods in a variety of applications. It is the decision problem of determining whether a Boolean formula has a satisfying assignment, believed to require exponentially

更新日期：2020-11-13
• arXiv.cs.ET Pub Date : 2020-11-12
Tae Woo Hong; Connor Myant; David Boyle

Fabricating 3D printed electronics using desktop printers has become more accessible with recent developments in conductive thermoplastic filaments. Because of their high resistance and difficulties in printing traces in vertical directions, most applications are restricted to capacitive sensing. In this paper, we introduce Thermoformed Circuit Board (TCB), a novel approach that employs the thermoformability

更新日期：2020-11-13
• arXiv.cs.ET Pub Date : 2020-11-11
Wesley H. Brigner; Naimul Hassan; Xuan Hu; Christopher H. Bennett; Felipe Garcia-Sanchez; Can Cui; Alvaro Velasquez; Matthew J. Marinella; Jean Anne C. Incorvia; Joseph S. Friedman

Complementary metal oxide semiconductor (CMOS) devices display volatile characteristics, and are not well suited for analog applications such as neuromorphic computing. Spintronic devices, on the other hand, exhibit both non-volatile and analog features, which are well-suited to neuromorphic computing. Consequently, these novel devices are at the forefront of beyond-CMOS artificial intelligence applications

更新日期：2020-11-13
• arXiv.cs.ET Pub Date : 2020-11-08
S. Rahimi Kari

The semiconductor and IC industry is facing the issue of high energy consumption. In modern days computers and processing systems are designed based on the Turing machine and Von Neumann's architecture. This architecture mainly focused on designing systems based on deterministic behaviors. To tackle energy consumption and reliability in systems, Stochastic Computing was introduced. In this research

更新日期：2020-11-12
• arXiv.cs.ET Pub Date : 2020-11-08
Sergi Abadal; Robert Guirado; Hamidreza Taghvaee; Akshay Jain; Elana Pereira de Santana; Peter Haring Bolívar; Mohamed Saeed; Renato Negra; Zhenxing Wang; Kun-Ta Wang; Max C. Lemme; Joshua Klein; Marina Zapater; Alexandre Levisse; David Atienza; Davide Rossi; Francesco Conti; Martino Dazzi; Geethan Karunaratne; Irem Boybat; Abu Sebastian

The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In such data-hungry multi-chip architectures, current Networks-in-Package (NiPs) may not be enough to cater to their heterogeneous and fast-changing communication

更新日期：2020-11-12
• arXiv.cs.ET Pub Date : 2020-11-06
Shilu Yan; Hongsheng Qi; Wei Cui

Quantum computing enables quantum neural networks (QNNs) to have great potentials to surpass artificial neural networks (ANNs). The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary

更新日期：2020-11-09
• arXiv.cs.ET Pub Date : 2020-11-05
Thomas F. Tiotto; Anouk S. Goossens; Jelmer P. Borst; Tamalika Banerjee; Niels A. Taatgen

Memristors have attracted interest as neuromorphic computation elements because they show promise in enabling efficient hardware implementations of artificial neurons and synapses. We performed measurements on interface-type memristors to validate their use in neuromorphic hardware. Specifically, we utilised Nb-doped SrTiO$_3$ memristors as synapses in a simulated neural network by arranging them into

更新日期：2020-11-06
• arXiv.cs.ET Pub Date : 2020-11-03
Héctor Iván García Hernández; Raymundo Torres Ruiz; Guo-Hua Sun

Quantum Computing and especially Quantum Machine Learning, in a short period of time, has gained a lot of interest through research groups around the world. This can be seen in the increasing number of proposed models for pattern classification applying quantum principles to a certain degree. Despise the increasing volume of models, there is a void in testing these models on real datasets and not only

更新日期：2020-11-06
• arXiv.cs.ET Pub Date : 2020-10-30
Filip Lemic; Sergi Abadal; Chong Han; Johann Marquez-Barja; Eduard Alarcón; Jeroen Famaey

Software-Defined Metamaterials (SDMs) show a strong potential for advancing the engineered control of electromagnetic waves. As such, they are envisioned to enable a variety of exciting applications, among others in the domains of smart textiles, high-resolution structural monitoring, and sensing in challenging environments. Many of the applications envisage deformations of the SDM structure, such

更新日期：2020-11-05
• arXiv.cs.ET Pub Date : 2020-11-03

One of the requirements imposed on the realistic quantum computers is to provide computation results which can be repeated and reproduced. In the situation when one needs to repeat the quantum computation procedure several times, it is crucial that the copies of the quantum devices are similar in the sense of the produced results. In this work, we describe a simple procedure for based on variational

更新日期：2020-11-04
• arXiv.cs.ET Pub Date : 2020-11-02
Xinyu Huang; Yuting Fang; Adam Noel; Nan Yang

This paper proposes a novel imperfect spherical transmitter (TX) model, namely the membrane fusion (MF)-based TX, that adopts MF between a vesicle and the TX membrane to release molecules encapsulated within the vesicle. For the MF-based TX, the molecule release probability and the fraction of molecules released from the TX membrane are derived. Incorporating molecular degradation and a fully-absorbing

更新日期：2020-11-03
• arXiv.cs.ET Pub Date : 2020-11-02
Javad Zarrin; Phang Hao Wen; Lakshmi Babu Saheer; Bahram Zarrin

Blockchain has made an impact on today's technology by revolutionizing the financial industry in its utilization on cryptocurrency and the features it provided on decentralization. With the current trend of pursuing the decentralized Internet, many methods have been proposed to achieve decentralization considering different aspects of the current Internet model ranging from infrastructure and protocols

更新日期：2020-11-03
• arXiv.cs.ET Pub Date : 2020-11-01
Joshua Mack; Ruben Purdy; Kris Rockowitz; Michael Inouye; Edward Richter; Spencer Valancius; Nirmal Kumbhare; Md Sahil Hassan; Kaitlin Fair; John Mixter; Ali Akoglu

Neuromorphic architectures have been introduced as platforms for energy efficient spiking neural network execution. The massive parallelism offered by these architectures has also triggered interest from non-machine learning application domains. In order to lift the barriers to entry for hardware designers and application developers we present RANC: a Reconfigurable Architecture for Neuromorphic Computing

更新日期：2020-11-03
• arXiv.cs.ET Pub Date : 2020-11-01
Hyokeun Lee; Seungyong Lee; Moonsoo Kim; Hyun Kim; Hyuk-Jae Lee

With the growing demand for technology scaling and storage capacity in server systems to support high-performance computing, phase-change memory (PCM) has garnered attention as the next-generation non-volatile memory to satisfy these requirements. However, write disturbance error (WDE) appears as a serious reliability problem preventing PCM from general commercialization. WDE occurs on the neighboring

更新日期：2020-11-03
• arXiv.cs.ET Pub Date : 2020-10-31
Shamiul Alam; Md Shafayat Hossain; Ahmedullah Aziz

The interplay between ferromagnetism and topological properties of electronic band structures leads to a precise quantization of Hall resistance without any external magnetic field. This so-called quantum anomalous Hall effect (QAHE) is born out of topological correlations, and is oblivious of low-sample quality. It was envisioned to lead towards dissipationless and topologically protected electronics

更新日期：2020-11-03
• arXiv.cs.ET Pub Date : 2020-09-16
Felix Köster; Dominik Ehlert; Kathy Lüdge

We analyze the memory capacity of a delay based reservoir computer with a Hopf normal form as nonlinearity and numerically compute the linear as well as the higher order recall capabilities. A possible physical realisation could be a laser with external cavity, for which the information is fed via electrical injection. A task independent quantification of the computational capability of the reservoir

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-28
Sebastian Lotter; Maximilian Schäfer; Johannes Zeitler; Robert Schober

Synaptic communication is a natural Molecular Communication (MC) system which may serve as a blueprint for the design of synthetic MC systems. In particular, it features highly specialized mechanisms to enable inter-symbol interference (ISI)-free and energy efficient communication. The understanding of synaptic MC is furthermore critical for disruptive innovations in the context of brain-machine interfaces

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-09-28

Quantum computing is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum-mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of quantum computing will help to solve many complex and computationally intractable problems in several areas of research

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-27
Julian Büchel; Jonathan Kakon; Michel Perez; Giacomo Indiveri

Efficient Balanced Networks (EBNs) are networks of spiking neurons in which excitatory and inhibitory synaptic currents are balanced on a short timescale, leading to desirable coding properties such as high encoding precision, low firing rates, and distributed information representation. It is for these benefits that it would be desirable to implement such networks in low-power neuromorphic processors

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-25
Jonghyeon Lee; Taewon Kang

The commercialization of transistors capable of both switching and amplification in 1960 resulted in the development of second-generation computers, which resulted in the miniaturization and lightening while accelerating the reduction and development of production costs. However, the self-resistance and the resistance used in conjunction with semiconductors, which are the basic principles of computers

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-23
Clarice D. Aiello; D. D. Awschalom; Hannes Bernien; Tina Brower-Thomas; Kenneth R. Brown; Todd A. Brun; Justin R. Caram; Eric Chitambar; Rosa Di Felice; Michael F. J. Fox; Stephan Haas; Alexander W. Holleitner; Eric R. Hudson; Jeffrey H. Hunt; Robert Joynt; Scott Koziol; H. J. Lewandowski; Douglas T. McClure; Jens Palsberg; Gina Passante; Kristen L. Pudenz; Christopher J. K. Richardson; Jessica L.

Interest in building dedicated Quantum Information Science and Engineering (QISE) education programs has greatly expanded in recent years. These programs are inherently convergent, complex, often resource intensive and likely require collaboration with a broad variety of stakeholders. In order to address this combination of challenges, we have captured ideas from many members in the community. This

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-25
Shion Maeda; Nicolas Chauvet; Hayato Saigo; Hirokazu Hori; Guillaume Bachelier; Serge Huant; Makoto Naruse

Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-23
Özlem Salehi; Zeki Seskir; İlknur Tepe

Quantum computing is a topic mainly rooted in physics, and it has been gaining rapid popularity in recent years. A need for extending the educational reach to groups outside of physics has also been becoming a necessity. Following this, a shift in educational mindset considering teaching quantum computing as a generalized probability theory rather than a field emanating from physics is beginning to

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-24
Suresh Nambi; Salim Ullah; Aditya Lohana; Siva Satyendra Sahoo; Farhad Merchant; Akash Kumar

The recent advances in machine learning, in general, and Artificial Neural Networks (ANN), in particular, has made smart embedded systems an attractive option for a larger number of application areas. However, the high computational complexity, memory footprints, and energy requirements of machine learning models hinder their deployment on resource-constrained embedded systems. Most state-of-the-art

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-24
Syuan-Hao Sie; Jye-Luen Lee; Yi-Ren Chen; Chih-Cheng Lu; Chih-Cheng Hsieh; Meng-Fan Chang; Kea-Tiong Tang

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the large storage overheads and the substantial computation cost of CNNs are problematic in hardware accelerators. Computing-in-memory (CIM) architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. However, the intensive multiply and accumulation (MAC) operations

更新日期：2020-10-30
• arXiv.cs.ET Pub Date : 2020-10-22
Martin Kong

Most quantum compiler transformations and qubit allocation techniques to date are either peep-hole focused or rely on sliding windows that depend on a number of external parameters. Thus, global optimization criteria are still lacking. In this paper we explore the synergies and impact of affine loop transformations in the context of qubit allocation and mapping. With this goal in mind, we have implemented

更新日期：2020-10-26
• arXiv.cs.ET Pub Date : 2020-10-21
Ana Neri; Rui Soares Barbosa; José N. Oliveira

Based on the connection between the categorical derivation of classical programs from specifications and the category-theoretic approach to quantum physics, this paper contributes to extending the laws of classical program algebra to quantum programming. This aims at building correct-by-construction quantum circuits to be deployed on quantum devices such as those available at the IBM Q Experience.

更新日期：2020-10-26
• arXiv.cs.ET Pub Date : 2020-10-16
Christos Liaskos; Shuai Niez; Ageliki Tsioliaridou; Andreas Pitsillides; Sotiris Ioannidis; Ian F. Akyildiz

Intelligent surfaces exert deterministic control over the wireless propagation phenomenon, enabling novel capabilities in performance, security and wireless power transfer. Such surfaces come in the form of rectangular tiles that cascade to cover large surfaces such as walls, ceilings or building facades. Each tile is addressable and can receive software commands from a controller, manipulating an

更新日期：2020-10-26
• arXiv.cs.ET Pub Date : 2020-10-17
Jeffrey M. Shainline

To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent. Using light for communication enables high fan-out as well as low-latency signaling across

更新日期：2020-10-20
• arXiv.cs.ET Pub Date : 2020-10-16
Krzysztof Domino; Mátyás Koniorczyk; Krzysztof Krawiec; Konrad Jałowiecki; Bartłomiej Gardas

We consider a railway dispatching problem: delay and conflict management on a single-track railway line. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the segment being considered. This is a computationally hard problem and its solution is needed in a very short time in practice. We introduce a quadratic unconstrained binary optimization

更新日期：2020-10-19
• arXiv.cs.ET Pub Date : 2020-10-15
Nivedita Dey; Mrityunjay Ghosh; Subhra Samir kundu; Amlan Chakrabarti

The magnificence grandeur of quantum computing lies in the inherent nature of quantum particles to exhibit true parallelism, which can be realized by indubitably fascinating theories of quantum physics. The possibilities opened by quantum computation (QC) is no where analogous to any classical simulation as quantum computers can efficiently simulate the complex dynamics of strongly correlated inter-facial

更新日期：2020-10-19
• arXiv.cs.ET Pub Date : 2020-10-14
Mrityunjay Ghosh; Nivedita Dey; Debdeep Mitra; Amlan Chakrabarti

Ant colony optimization is one of the potential solutions to tackle intractable NP-Hard discrete combinatorial optimization problems. The metaphor of ant colony can be thought of as the evolution of the best path from a given graph as a globally optimal solution, which is unaffected by earlier local convergence to achieve improved optimization efficiency. Earlier Quantum Ant Colony Optimization research

更新日期：2020-10-17
• arXiv.cs.ET Pub Date : 2020-10-12
Mariia Mykhailova; Krysta M. Svore

Quantum computing harnesses quantum laws of nature to enable new types of algorithms, not efficiently possible on traditional computers, that may lead to breakthroughs in crucial areas like materials science and chemistry. There is rapidly growing demand for a quantum workforce educated in the basics of quantum computing, in particular in quantum programming. However, there are few offerings for non-specialists

更新日期：2020-10-17
• arXiv.cs.ET Pub Date : 2020-10-12

The social worker scheduling problem is a class of combinatorial optimization problems that combines scheduling with routing issues. These types of problems with classical computing can only be solved, in the best of cases, in an approximate way and significantly when the input data does not grow considerably. Today, the focus on the quantum computer should no longer be only on its enormous computing

更新日期：2020-10-13
• arXiv.cs.ET Pub Date : 2020-10-09
Twisha Titirsha; Anup Das

Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SNNs), making these hardware platforms particularly suitable for embedded systems and other energy-constrained environments. We observe that the long bitlines and wordlines in a crossbar of the hardware create significant current

更新日期：2020-10-13
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