-
Image-based Approximate DNA Storage System arXiv.cs.ET Pub Date : 2021-03-04 Bingzhe Li; Li Ou; David Du
Deoxyribonucleic Acid (DNA) as a storage medium with high density and long-term preservation properties can satisfy the requirement of archival storage for rapidly increased digital volume. The read and write processes of DNA storage are error-prone. Images widely used in social media have the properties of fault tolerance which are well fitted to the DNA storage. However, prior work simply investigated
-
Experimental Body-input Three-stage DC offset Calibration Scheme for Memristive Crossbar arXiv.cs.ET Pub Date : 2021-03-03 Charanraj Mohan; L. A. Camuñas-Mesa; Elisa Vianello; Carlo Reita; José M. de la Rosa; Teresa Serrano-Gotarredona; Bernabé Linares-Barranco
Reading several ReRAMs simultaneously in a neuromorphic circuit increases power consumption and limits scalability. Applying small inference read pulses is a vain attempt when offset voltages of the read-out circuit are decisively more. This paper presents an experimental validation of a three-stage calibration scheme to calibrate the DC offset voltage across the rows of the memristive crossbar. The
-
Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network arXiv.cs.ET Pub Date : 2021-03-03 Sourav De; Yao-Jen Lee; Darsen D. Lu
This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (NN) with 96.4% inference accuracy on the MNIST dataset as the baseline. As an aftermath of temperature change, the conductance drift of a programmed
-
Reservoir Computing with Superconducting Electronics arXiv.cs.ET Pub Date : 2021-03-03 Graham E. Rowlands; Minh-Hai Nguyen; Guilhem J. Ribeill; Andrew P. Wagner; Luke C. G. Govia; Wendson A. S. Barbosa; Daniel J. Gauthier; Thomas A. Ohki
The rapidity and low power consumption of superconducting electronics makes them an ideal substrate for physical reservoir computing, which commandeers the computational power inherent to the evolution of a dynamical system for the purposes of performing machine learning tasks. We focus on a subset of superconducting circuits that exhibit soliton-like dynamics in simple transmission line geometries
-
PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form arXiv.cs.ET Pub Date : 2021-03-02 Mashiyat Zaman; Kotaro Tanahashi; Shu Tanaka
We present PyQUBO, an open-source, Python library for constructing quadratic unconstrained binary optimizations (QUBOs) from the objective functions and the constraints of optimization problems. PyQUBO enables users to prepare QUBOs or Ising models for various combinatorial optimization problems with ease thanks to the abstraction of expressions and the extensibility of the program. QUBOs and Ising
-
Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices arXiv.cs.ET Pub Date : 2021-03-01 C. Mohan; L. A. Camuñas-Mesa; J. M. de la Rosa; T. Serrano-Gotarredona; B. Linares-Barranco
The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired neuromorphic systems. Large-scale neuromorphic hardware platforms are being developed with increasing number of neurons and synapses, having a critical bottleneck in the online learning
-
Large-scale Quantum Approximate Optimization via Divide-and-Conquer arXiv.cs.ET Pub Date : 2021-02-26 Junde Li; Mahabubul Alam; Swaroop Ghosh
Quantum Approximate Optimization Algorithm (QAOA) is a promising hybrid quantum-classical algorithm for solving combinatorial optimization problems. However, it cannot overcome qubit limitation for large-scale problems. Furthermore, the execution time of QAOA scales exponentially with the problem size. We propose a Divide-and-Conquer QAOA (DC-QAOA) to address the above challenges for graph maximum
-
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks arXiv.cs.ET Pub Date : 2021-02-26 Jinlin Xiang; Shane Colburn; Arka Majumdar; Eli Shlizerman
In recent years, Convolutional Neural Networks (CNNs) have enabled ubiquitous image processing applications. As such, CNNs require fast runtime (forward propagation) to process high-resolution visual streams in real time. This is still a challenging task even with state-of-the-art graphics and tensor processing units. The bottleneck in computational efficiency primarily occurs in the convolutional
-
An experimental demonstration of the memristor test arXiv.cs.ET Pub Date : 2021-02-23 Y. V. Pershin; J. Kim; T. Datta; M. Di Ventra
A simple and unambiguous test has been recently suggested [J. Phys. D: Applied Physics, 52, 01LT01 (2018)] to check experimentally if a resistor with memory is indeed a memristor, namely a resistor whose resistance depends only on the charge that flows through it, or on the history of the voltage across it. However, although such a test would represent the litmus test for claims about memristors (in
-
Quantum Entropic Causal Inference arXiv.cs.ET Pub Date : 2021-02-23 Mohammad Ali Javidian; Vaneet Aggarwal; Fanglin Bao; Zubin Jacob
As quantum computing and networking nodes scale-up, important open questions arise on the causal influence of various sub-systems on the total system performance. These questions are related to the tomographic reconstruction of the macroscopic wavefunction and optimizing connectivity of large engineered qubit systems, the reliable broadcasting of information across quantum networks as well as speed-up
-
Dimensions of Timescales in Neuromorphic Computing Systems arXiv.cs.ET Pub Date : 2021-02-21 Herbert Jaeger; Dirk Doorakkers; Celestine Lawrence; Giacomo Indiveri
This article is a public deliverable of the EU project "Memory technologies with multi-scale time constants for neuromorphic architectures" (MeMScales, https://memscales.eu, Call ICT-06-2019 Unconventional Nanoelectronics, project number 871371). This arXiv version is a verbatim copy of the deliverable report, with administrative information stripped. It collects a wide and varied assortment of phenomena
-
All-Chalcogenide Programmable All-Optical Deep Neural Networks arXiv.cs.ET Pub Date : 2021-02-20 Ting Y; Xiaoxuan M; Ernest Pastor; Jonathan K. George; Simon Wall; Mario Miscuglio; Robert E. Simpson; Volker J. Sorger
Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-optic conversions could alleviate these shortcomings. However, an all-optical nonlinear activation function, which is a vital building block for optical
-
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning arXiv.cs.ET Pub Date : 2021-02-19 D. Dang; S. V. R. Chittamuru; S. Pasricha; R. Mahapatra; D. Sahoo
Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete
-
Neuromorphic Pattern Generation Circuits for Bioelectronic Medicine arXiv.cs.ET Pub Date : 2021-02-18 Elisa Donati; Renate Krause; Giacomo Indiveri
Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. These circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe
-
Photonic Convolution Neural Network Based on Interleaved Time-Wavelength Modulation arXiv.cs.ET Pub Date : 2021-02-18 Yue Jiang; Wenjia Zhang; Fan Yang; Zuyuan He
Convolution neural network (CNN), as one of the most powerful and popular technologies, has achieved remarkable progress for image and video classification since its invention in 1989. However, with the high definition video-data explosion, convolution layers in the CNN architecture will occupy a great amount of computing time and memory resources due to high computation complexity of matrix multiply
-
Scalability of all-optical neural networks based on spatial light modulators arXiv.cs.ET Pub Date : 2021-02-19 Ying Zuo; Zhao Yujun; You-Chiuan Chen; Shengwang Du; Junwei Liu
Optical implementation of artificial neural networks has been attracting great attention due to its potential in parallel computation at speed of light. Although all-optical deep neural networks (AODNNs) with a few neurons have been experimentally demonstrated with acceptable errors recently, the feasibility of large scale AODNNs remains unknown because error might accumulate inevitably with increasing
-
All-optical spiking neurosynaptic networks with self-learning capabilities arXiv.cs.ET Pub Date : 2021-02-18 J. Feldmann; N. Youngblood; C. D. Wright; H. Bhaskaran; W. H. P. Pernice
Software-implementation, via neural networks, of brain-inspired computing approaches underlie many important modern-day computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, differing from real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast
-
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks arXiv.cs.ET Pub Date : 2021-02-18 Shreyas Chaudhari; Harideep Nair; José M. F. Moura; John Paul Shen
Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc. These applications typically involve small, low-power devices on the edge that collect and process real-time sensory signals. State-of-the-art time-series clustering methods perform some form of loss minimization that is extremely computationally intensive
-
Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction arXiv.cs.ET Pub Date : 2021-02-17 Corey Lammie; Wei Xiang; Mostafa Rahimi Azghadi
The unpredictability of seizures continues to distress many people with drug-resistant epilepsy. On account of recent technological advances, considerable efforts have been made using different hardware technologies to realize smart devices for the real-time detection and prediction of seizures. In this paper, we investigate the feasibility of using Memristive Deep Learning Systems (MDLSs) to perform
-
IRS-Assisted Wireless Powered NOMA: Is Dynamic Passive Beamforming Really Needed? arXiv.cs.ET Pub Date : 2021-02-17 Qingqing Wu; Xiaobo Zhou; Robert Schober
Intelligent reflecting surface (IRS) is a promising technology to improve the performance of wireless powered communication networks (WPCNs) due to its capability to reconfigure signal propagation environments via smart reflection. In particular, the high passive beamforming gain promised by IRS can significantly enhance the efficiency of both downlink wireless power transfer (DL WPT) and uplink wireless
-
Orchestrated Trios: Compiling for Efficient Communication in Quantum Programs with 3-Qubit Gates arXiv.cs.ET Pub Date : 2021-02-16 Casey Duckering; Jonathan M. Baker; Andrew Litteken; Frederic T. Chong
Current quantum computers are especially error prone and require high levels of optimization to reduce operation counts and maximize the probability the compiled program will succeed. These computers only support operations decomposed into one- and two-qubit gates and only two-qubit gates between physically connected pairs of qubits. Typical compilers first decompose operations, then route data to
-
ReGraphX: NoC-enabled 3D Heterogeneous ReRAM Architecture for Training Graph Neural Networks arXiv.cs.ET Pub Date : 2021-02-16 Aqeeb Iqbal Arka; Biresh Kumar Joardar; Janardhan Rao Doppa; Partha Pratim Pande; Krishnendu Chakrabarty
Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs) operating on graphs. However, GNNs are more complex compared to traditional DNNs as they simultaneously exhibit features of both DNN and graph applications. As a result, architectures specifically optimized for either DNNs or graph applications are not suited for GNN training. In this work, we propose a 3D heterogeneous manycore
-
PCM-trace: Scalable Synaptic Eligibility Traceswith Resistivity Drift of Phase-Change Materials arXiv.cs.ET Pub Date : 2021-02-14 Yigit Demirag; Filippo Moro; Thomas Dalgaty; Gabriele Navarro; Charlotte Frenkel; Giacomo Indiveri; Elisa Vianello; Melika Payvand
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, it is important to develop appropriate on-chip learning
-
About using analog computers in today's largest computational challenges arXiv.cs.ET Pub Date : 2021-02-14 Sven Köppel; Bernd Ulmann; Lars Heimann; Dirk Killat
Analog computers perceive a revival as a feasible technology platform for low precision, energy efficient and fast computing. We quantify this statement by measuring the performance of a modern analog computer and comparing it with traditional digital processors. General statements are made about ordinary and partial differential equations. As an example for large scale scientific computing applications
-
CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator arXiv.cs.ET Pub Date : 2021-02-13 Febin Sunny; Asif Mirza; Mahdi Nikdast; Sudeep Pasricha
Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and inference performance compared to CPUs and GPUs. In this paper, we propose a novel cross-layer optimized neural network accelerator called CrossLight that leverages silicon photonics. CrossLight includes device-level engineering for resilience to process variations and
-
Dynamic Precision Analog Computing for Neural Networks arXiv.cs.ET Pub Date : 2021-02-12 Sahaj Garg; Joe Lou; Anirudh Jain; Mitchell Nahmias
Analog electronic and optical computing exhibit tremendous advantages over digital computing for accelerating deep learning when operations are executed at low precision. In this work, we derive a relationship between analog precision, which is limited by noise, and digital bit precision. We propose extending analog computing architectures to support varying levels of precision by repeating operations
-
Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks arXiv.cs.ET Pub Date : 2021-02-10 Vinay Joshi; Wangxin He; Jae-sun Seo; Bipin Rajendran
The cost involved in training deep neural networks (DNNs) on von-Neumann architectures has motivated the development of novel solutions for efficient DNN training accelerators. We propose a hybrid in-memory computing (HIC) architecture for the training of DNNs on hardware accelerators that results in memory-efficient inference and outperforms baseline software accuracy in benchmark tasks. We introduce
-
Hardware-aware in-situ Boltzmann machine learning using stochastic magnetic tunnel junctions arXiv.cs.ET Pub Date : 2021-02-09 Jan Kaiser; William A. Borders; Kerem Y. Camsari; Shunsuke Fukami; Hideo Ohno; Supriyo Datta
One of the big challenges of current electronics is the design and implementation of hardware neural networks that perform fast and energy-efficient machine learning. Spintronics is a promising catalyst for this field with the capabilities of nanosecond operation and compatibility with existing microelectronics. Considering large-scale, viable neuromorphic systems however, variability of device properties
-
Free-space optical neural network based on thermal atomic nonlinearity arXiv.cs.ET Pub Date : 2021-02-08 Albert Ryou; James Whitehead; Maksym Zhelyeznyakov; Paul Anderson; Cem Keskin; Michal Bajcsy; Arka Majumdar
As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption due to intrinsic parallelism of free-space optics. At the
-
Directed percolation and numerical stability of simulations of digital memcomputing machines arXiv.cs.ET Pub Date : 2021-02-06 Yuan-Hang Zhang; Massimiliano Di Ventra
Digital memcomputing machines (DMMs) are a novel, non-Turing class of machines designed to solve combinatorial optimization problems. They can be physically realized with continuous-time, non-quantum dynamical systems with memory (time non-locality), whose ordinary differential equations (ODEs) can be numerically integrated on modern computers. Solutions of many hard problems have been reported by
-
Multi-state MRAM cells for hardware neuromorphic computing arXiv.cs.ET Pub Date : 2021-02-05 Piotr Rzeszut; Jakub Chęciński; Ireneusz Brzozowski; Sławomir Ziętek; Witold Skowroński; Tomasz Stobiecki
Magnetic tunnel junctions (MTJ) have been successfully applied in various sensing application and digital information storage technologies. Currently, a number of new potential applications of MTJs are being actively studied, including high-frequency electronics, energy harvesting or random number generators. Recently, MTJs have been also proposed in designs of a new platforms for unconventional or
-
Optical Stochastic Computing Architectures Using Photonic Crystal Nanocavities arXiv.cs.ET Pub Date : 2021-02-03 Hassnaa El-Derhalli; Lea Constans; Sebastien Le Beux; Alfredo De Rossi; Fabrice Raineri; Sofiene Tahar
Stochastic computing allows a drastic reduction in hardware complexity using serial processing of bit streams. While the induced high computing latency can be overcome using integrated optics technology, the design of realistic optical stochastic computing architectures calls for energy efficient switching devices. Photonics Crystal (PhC) nanocavities are $\mu m^2$ scale devices offering 100fJ switching
-
Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs arXiv.cs.ET Pub Date : 2021-02-02 Guodong Yin; Yi Cai; Juejian Wu; Zhengyang Duan; Zhenhua Zhu; Yongpan Liu; Yu Wang; Huazhong Yang; Xueqing Li
Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications. Compared to current-domain CiM solutions, charge-domain CiM shows the opportunity for higher energy efficiency and resistance to device variations. However, the area occupation and standby leakage power of existing SRAMbased charge-domain CiM (CD-CiM) are high. This paper proposes
-
Measurement-based Uncomputation Applied to Controlled Modular Multiplication arXiv.cs.ET Pub Date : 2021-02-02 Panjin Kim; Daewan Han
This is a brief report on a particular use of measurement-based uncomputation. Though not appealing in performance, it may shed light on optimization techniques in various quantum circuits.
-
DisQ: A Novel Quantum Output State Classification Method on IBM Quantum Computers using OpenPulse arXiv.cs.ET Pub Date : 2021-02-01 Tirthak Patel; Devesh Tiwari
Superconducting quantum computing technology has ushered in a new era of computational possibilities. While a considerable research effort has been geared toward improving the quantum technology and building the software stack to efficiently execute quantum algorithms with reduced error rate, effort toward optimizing how quantum output states are defined and classified for the purpose of reducing the
-
Comparison of Cloud-Based Ion Trap and Superconducting Quantum Computer Architectures arXiv.cs.ET Pub Date : 2021-01-31 S. Blinov; B. Wu; C. Monroe
Quantum computing represents a radical departure from conventional approaches to information processing, offering the potential for solving problems that can never be approached classically. While large scale quantum computer hardware is still in development, several quantum computing systems have recently become available as commercial cloud services. We compare the performance of these systems on
-
Reservoir Computing with Thin-film Ferromagnetic Devices arXiv.cs.ET Pub Date : 2021-01-29 Matthew Dale; Richard F. L. Evans; Sarah Jenkins; Simon O'Keefe; Angelika Sebald; Susan Stepney; Fernando Torre; Martin Trefzer
Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential for extreme parallelism and ultra-low power consumption. Physical reservoir computing
-
Theory of heterogeneous circuits with probabilistic memristive devices arXiv.cs.ET Pub Date : 2021-01-28 V. A. Slipko; Y. V. Pershin
We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous probabilistic circuits, namely, the circuits combining binary or multi-state probabilistic memristive devices and continuum reactive components (capacitors and/or inductors). Such circuits are described in terms of occupation probabilities of memristive states that are functions of reactive variables. As an illustrative
-
Enabling Dataflow Optimization for Quantum Programs arXiv.cs.ET Pub Date : 2021-01-26 David Ittah; Thomas Häner; Vadym Kliuchnikov; Torsten Hoefler
We propose an IR for quantum computing that directly exposes quantum and classical data dependencies for the purpose of optimization. Our IR consists of two dialects, one input dialect and one that is specifically tailored to enable quantum-classical co-optimization. While the first employs a perhaps more intuitive memory-semantics (quantum operations act as side-effects), the latter uses value-semantics
-
The Granularity Gap Problem: A Hurdle for Applying Approximate Memory to Complex Data Layout arXiv.cs.ET Pub Date : 2021-01-26 Soramichi Akiyama; Ryota Shioya
The main memory access latency has not much improved for more than two decades while the CPU performance had been exponentially increasing until recently. Approximate memory is a technique to reduce the DRAM access latency in return of losing data integrity. It is beneficial for applications that are robust to noisy input and intermediate data such as artificial intelligence, multimedia processing
-
Static Analysis of Quantum Programs via Gottesman Types arXiv.cs.ET Pub Date : 2021-01-22 Robert Rand; Aarthi Sundaram; Kartik Singhal; Brad Lackey
The Heisenberg representation of quantum operators provides a powerful technique for reasoning about quantum circuits, albeit those restricted to the common (non-universal) Clifford set $H$, $S$ and $CNOT$. The Gottesman-Knill theorem showed that we can use this representation to efficiently simulate Clifford circuits. We show that Gottesman's semantics for quantum programs can be treated as a type
-
Efficient, stabilized two-qubit gates on a trapped-ion quantum computer arXiv.cs.ET Pub Date : 2021-01-19 Reinhold Blümel; Nikodem Grzesiak; Nhung H. Nguyen; Alaina M. Green; Ming Li; Andrii Maksymov; Norbert M. Linke; Yunseong Nam
Quantum computing is currently limited by the cost of two-qubit entangling operations. In order to scale up quantum processors and achieve a quantum advantage, it is crucial to economize on the power requirement of two-qubit gates, make them robust to drift in experimental parameters, and shorten the gate times. In this paper, we present two methods, one exact and one approximate, to construct optimal
-
SEMULATOR: Emulating the Dynamics of Crossbar Array-based Analog Neural System with Regression Neural Networks arXiv.cs.ET Pub Date : 2021-01-19 Chaeun Lee; Seyoung Kim
As deep neural networks require tremendous amount of computation and memory, analog computing with emerging memory devices is a promising alternative to digital computing for edge devices. However, because of the increasing simulation time for analog computing system, it has not been explored. To overcome this issue, analytically approximated simulators are developed, but these models are inaccurate
-
Quantum Permutation Synchronization arXiv.cs.ET Pub Date : 2021-01-19 Tolga Birdal; Vladislav Golyanik; Christian Theobalt; Leonidas Guibas
We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. In particular, we focus on permutation synchronization which involves solving a non-convex optimization problem in discrete variables. We start by formulating synchronization into a quadratic unconstrained binary optimization problem (QUBO). While such formulation respects the
-
Device Variability Analysis for Memristive Material Implication arXiv.cs.ET Pub Date : 2021-01-18 Simon Michael Laube; Nima TaheriNejad
Currently, memristor devices suffer from variability between devices and from cycle to cycle. In this work, we study the impact of device variations on memristive Material Implication (IMPLY). New constraints for different parameters and variables are analytically derived and compared to extensive simulation results, covering single gate and 1T1R crossbar structures. We show that a static analysis
-
Dynamic Ternary Content-Addressable Memory Is Indeed Promising: Design and Benchmarking Using Nanoelectromechanical Relays arXiv.cs.ET Pub Date : 2021-01-16 Hongtao Zhong; Shengjie Cao; Huazhong Yang; Xueqing Li
Ternary content addressable memory (TCAM) has been a critical component in caches, routers, etc., in which density, speed, power efficiency, and reliability are the major design targets. There have been the conventional low-write-power but bulky SRAM-based TCAM design, and also denser but less reliable or higher-write-power TCAM designs using nonvolatile memory (NVM) devices. Meanwhile, some TCAM designs
-
Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation arXiv.cs.ET Pub Date : 2021-01-08 Samuel Liu; Christopher H. Bennett; Joseph S. Friedman; Matthew J. Marinella; David Paydarfar; Jean Anne C. Incorvia
Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial
-
On the realistic worst case analysis of quantum arithmetic circuits arXiv.cs.ET Pub Date : 2021-01-12 Alexandru Paler; Oumarou Oumarou; Robert Basmadjian
We provide evidence that commonly held intuitions when designing quantum circuits can be misleading. In particular we show that: a) reducing the T-count can increase the total depth; b) it may be beneficial to trade CNOTs for measurements in NISQ circuits; c) measurement-based uncomputation of relative phase Toffoli ancillae can make up to 30\% of a circuit's depth; d) area and volume cost metrics
-
NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi arXiv.cs.ET Pub Date : 2021-01-12 Bodo Rueckauer; Connor Bybee; Ralf Goettsche; Yashwardhan Singh; Joyesh Mishra; Andreas Wild
Spiking Neural Networks (SNNs) are a promising paradigm for efficient event-driven processing of spatio-temporally sparse data streams. SNNs have inspired the design and can take advantage of the emerging class of neuromorphic processors like Intel Loihi. These novel hardware architectures expose a variety of constraints that affect firmware, compiler and algorithm development alike. To enable rapid
-
Quantum Consensus: an overview arXiv.cs.ET Pub Date : 2021-01-11 Marco Marcozzi; Leonardo Mostarda
We review the literature about reaching agreement in quantum networks, also called quantum consensus. After a brief introduction to the key feature of quantum computing, allowing the reader with no quantum theory background to have minimal tools to understand, we report a formal definition of quantum consensus and the protocols proposed. Proposals are classified according to the quantum feature used
-
On Interfacing the Brain with Quantum Computers: An Approach to Listen to the Logic of the Mind arXiv.cs.ET Pub Date : 2020-12-22 Eduardo Reck Miranda
This chapter presents a quantum computing-based approach to study and harness neuronal correlates of mental activity for the development of Brain-Computer Interface (BCI) systems. It introduces the notion of a logic of the mind, where neurophysiological data are encoded as logical expressions representing mental activity. Effective logical expressions are likely to be extensive, involving dozens of
-
A Thermodynamic Core using Voltage-Controlled Spin-Orbit-Torque Magnetic Tunnel Junctions arXiv.cs.ET Pub Date : 2021-01-10 Albert Lee; Bingqian Dai; Di Wu; Hao Wu; Robert N Schwartz; Kang L Wang
We present a magnetic implementation of a thermodynamic computing fabric. Magnetic devices within computing cores harness thermodynamics through its voltage-controlled thermal stability; while the evolution of network states is guided by the spin-orbit-torque effect. We theoretically derive the dynamics of the cores and show that the computing fabric can successfully compute ground states of a Boltzmann
-
Quantum Generative Models for Small Molecule Drug Discovery arXiv.cs.ET Pub Date : 2021-01-09 Junde Li; Rasit Topaloglu; Swaroop Ghosh
Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space which could be on the order of 1060 . Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting
-
Design of Full-Duplex Millimeter-Wave Integrated Access and Backhaul Networks arXiv.cs.ET Pub Date : 2021-01-08 Junkai Zhang; Navneet Garg; Mark Holm; Tharmalingam Ratnarajah
One of the key technologies for the future cellular networks is full-duplex (FD) enabled Integrated Access and Backhaul (IAB) networks operating in the millimeter-wave (mmWave) frequencies. The main challenge in realizing the FD-IAB networks is mitigating the impact of self-interference (SI) in the wideband mmWave frequencies. In this article, we first introduce the 3GPP IAB network architectures and
-
Neural Storage: A New Paradigm of Elastic Memory arXiv.cs.ET Pub Date : 2021-01-07 Prabuddha Chakraborty; Swarup Bhunia
Storage and retrieval of data in a computer memory plays a major role in system performance. Traditionally, computer memory organization is static - i.e., they do not change based on the application-specific characteristics in memory access behaviour during system operation. Specifically, the association of a data block with a search pattern (or cues) as well as the granularity of a stored data do
-
Translation of Quantum Circuits into Quantum Turing Machines for Deutsch and Deutsch-Jozsa Problems arXiv.cs.ET Pub Date : 2021-01-06 Giuseppe Corrente
We want in this article to show the usefulness of Quantum Turing Machine (QTM) in a high-level didactic context as well as in theoretical studies. We use QTM to show its equivalence with quantum circuit model for Deutsch and Deutsch-Jozsa algorithms. Further we introduce a strategy of translation from Quantum Circuit to Quantum Turing models by these examples. Moreover we illustrate some features of
-
Experimental System for Molecular Communication in Pipe Flow With Magnetic Nanoparticles arXiv.cs.ET Pub Date : 2021-01-06 Wayan Wicke; Harald Unterweger; Jens Kirchner; Lukas Brand; Arman Ahmadzadeh; Doaa Ahmed; Vahid Jamali; Christoph Alexiou; Georg Fischer; Robert Schober
In the emerging field of molecular communication (MC), testbeds are needed to validate theoretical concepts, motivate applications, and guide further modeling efforts. To this end, this paper presents a flexible and extendable in-vessel MC testbed based on superparamagnetic iron oxide nanoparticles (SPIONs) dispersed in an aqueous suspension as they are also used for drug targeting in biotechnology
-
Toward Location-aware In-body Terahertz Nanonetworks with Energy Harvesting arXiv.cs.ET Pub Date : 2021-01-06 Filip Lemic; Sergi Abadal; Eduard Alarcón; Jeroen Famaey
Nanoscale wireless networks are expected to revolutionize a variety of domains, with significant advances conceivable in in-body healthcare. In healthcare, these nanonetworks will consist of energy-harvesting nanodevices passively flowing through the bloodstream, taking actions at certain locations, and communicating results to more powerful Body Area Network (BAN) nodes. Assuming such a setup and
-
A Survey on Silicon Photonics for Deep Learning arXiv.cs.ET Pub Date : 2021-01-05 Febin P Sunny; Ebadollah Taheri; Mahdi Nikdast; Sudeep Pasricha
Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition. These achievements are the culmination of decades-long research into better training techniques and deeper neural network models, as well as improvements in hardware platforms that are used to train and execute the
-
Quantitative Evaluation of Hardware Binary Stochastic Neurons arXiv.cs.ET Pub Date : 2021-01-01 Orchi Hassan; Supriyo Datta; Kerem Y. Camsari
Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a ground-state search of the Ising model. A common theme of such Ising Machines is to tailor the physics of underlying hardware to the mathematics of the Ising model to improve some aspect of performance that is measured in speed
Contents have been reproduced by permission of the publishers.