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Blockchain-enabled Circular Economy -- Collaborative Responsibility in Solar Panel Recycling arXiv.cs.ET Pub Date : 2024-03-15 Mohammad Jabed Morshed Chowdhury, Naveed Ul Hassan, Wayes Tushar, Dustin Niyato, Tapan Saha, H Vincent Poor, Chau Yuen
The adoption of renewable energy resources, such as solar power, is on the rise. However, the excessive installation and lack of recycling facilities pose environmental risks. This paper suggests a circular economy approach to address the issue. By implementing blockchain technology, the end-of-life (EOL) of solar panels can be tracked, and responsibilities can be assigned to relevant stakeholders
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NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways arXiv.cs.ET Pub Date : 2024-03-14 Jiazhao Wang, Wenchao Jiang, Ruofeng Liu, Bin Hu, Demin Gao, Shuai Wang
A physical-layer modulator is a vital component for an IoT gateway to map the symbols to signals. However, due to the soldered hardware chipsets on the gateway's motherboards or the diverse toolkits on different platforms for the software radio, the existing solutions either have limited extensibility or are platform-specific. Such limitation is hard to ignore when modulation schemes and hardware platforms
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Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications arXiv.cs.ET Pub Date : 2024-03-12 Cong T. Nguyen, Yuris Mulya Saputra, Nguyen Van Huynh, Tan N. Nguyen, Dinh Thai Hoang, Diep N Nguyen, Van-Quan Pham, Miroslav Voznak, Symeon Chatzinotas, Dinh-Hieu Tran
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient
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Hybrid Data Management Architecture for Present Quantum Computing arXiv.cs.ET Pub Date : 2024-03-12 Markus Zajac, Uta Störl
Quantum computers promise polynomial or exponential speed-up in solving certain problems compared to classical computers. However, in practical use, there are currently a number of fundamental technical challenges. One of them concerns the loading of data into quantum computers, since they cannot access common databases. In this vision paper, we develop a hybrid data management architecture in which
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Rate-independent continuous inhibitory chemical reaction networks are Turing-universal arXiv.cs.ET Pub Date : 2024-03-11 Kim Calabrese, David Doty
We study the model of continuous chemical reaction networks (CRNs), consisting of reactions such as $A+B \to C+D$ that can transform some continuous, nonnegative real-valued quantity (called a *concentration*) of chemical species $A$ and $B$ into equal concentrations of $C$ and $D$. Such a reaction can occur from any state in which both reactants $A$ and $B$ are present, i.e., have positive concentration
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Integration of Physics-Derived Memristor Models with Machine Learning Frameworks arXiv.cs.ET Pub Date : 2024-03-11 Zhenming Yu, Stephan Menzel, John Paul Strachan, Emre Neftci
Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simulators can take device nonidealities into account and are integrated with modern ML frameworks. However, memristors in these simulators are modeled with either lookup tables or simple analytic models with basic nonlinearities
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The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming arXiv.cs.ET Pub Date : 2024-03-11 Zhenming Yu, Ming-Jay Yang, Jan Finkbeiner, Sebastian Siegel, John Paul Strachan, Emre Neftci
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time. However, on-chip training with memristor arrays still faces challenges, including device-to-device and cycle-to-cycle variations, switching non-linearity, and especially
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User Connection and Resource Allocation Optimization in Blockchain Empowered Metaverse over 6G Wireless Communications arXiv.cs.ET Pub Date : 2024-03-08 Liangxin Qian, Chang Liu, Jun Zhao
The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters
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Paving the Way for Pass Disturb Free Vertical NAND Storage via A Dedicated and String-Compatible Pass Gate arXiv.cs.ET Pub Date : 2024-03-08 Zijian Zhao, Sola Woo, Khandker Akif Aabrar, Sharadindu Gopal Kirtania, Zhouhang Jiang, Shan Deng, Yi Xiao, Halid Mulaosmanovic, Stefan Duenkel, Dominik Kleimaier, Steven Soss, Sven Beyer, Rajiv Joshi, Scott Meninger, Mohamed Mohamed, Kijoon Kim, Jongho Woo, Suhwan Lim, Kwangsoo Kim, Wanki Kim, Daewon Ha, Vijaykrishnan Narayanan, Suman Datta, Shimeng Yu, Kai Ni
In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of the applied field by channel at the low-${V}_{TH}$
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The Social Impact of Generative AI: An Analysis on ChatGPT arXiv.cs.ET Pub Date : 2024-03-07 Maria T. Baldassarre, Danilo Caivano, Berenice Fernandez Nieto, Domenico Gigante, Azzurra Ragone
In recent months, the social impact of Artificial Intelligence (AI) has gained considerable public interest, driven by the emergence of Generative AI models, ChatGPT in particular. The rapid development of these models has sparked heated discussions regarding their benefits, limitations, and associated risks. Generative models hold immense promise across multiple domains, such as healthcare, finance
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A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Health arXiv.cs.ET Pub Date : 2024-03-08 Alexander Marrapese, Basem Suleiman, Imdad Ullah, Juno Kim
Understanding the conversation abilities of Large Language Models (LLMs) can help lead to its more cautious and appropriate deployment. This is especially important for safety-critical domains like mental health, where someone's life may depend on the exact wording of a response to an urgent question. In this paper, we propose a novel framework for evaluating the nuanced conversation abilities of LLMs
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Nonlinear dynamics and stability analysis of locally-active Mott memristors using a physics-based compact model arXiv.cs.ET Pub Date : 2024-03-02 Wei Yi
Locally-active memristors are a class of emerging nonlinear dynamic circuit elements that hold promise for scalable yet biomimetic neuromorphic circuits. Starting from a physics-based compact model, we performed small-signal linearization analyses and applied Chua's local activity theory to a one-dimensional locally-active vanadium dioxide Mott memristor based on an insulator-to-metal phase transition
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Self-Assembly of Patterns in the abstract Tile Assembly Model arXiv.cs.ET Pub Date : 2024-02-26 Phillip Drake, Matthew J. Patitz, Scott M. Summers, Tyler Tracy
In the abstract Tile Assembly Model, self-assembling systems consisting of tiles of different colors can form structures on which colored patterns are ``painted.'' We explore the complexity, in terms of the unique tile types required, of assembling various patterns, proving several upper and lower bounds.
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Quantum computing in civil engineering: Limitations arXiv.cs.ET Pub Date : 2024-02-22 Joern Ploennigs, Markus Berger, Martin Mevissen, Kay Smarsly
Quantum computing is a new computational paradigm with the potential to solve certain computationally challenging problems much faster than traditional approaches. Civil engineering encompasses many computationally challenging problems, which leads to the question of how well quantum computing is suitable for solving civil engineering problems and how much impact and implications to the field of civil
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Neuromorphic Face Analysis: a Survey arXiv.cs.ET Pub Date : 2024-02-18 Federico Becattini, Lorenzo Berlincioni, Luca Cultrera, Alberto Del Bimbo
Neuromorphic sensors, also known as event cameras, are a class of imaging devices mimicking the function of biological visual systems. Unlike traditional frame-based cameras, which capture fixed images at discrete intervals, neuromorphic sensors continuously generate events that represent changes in light intensity or motion in the visual field with high temporal resolution and low latency. These properties
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Enabling data-driven and bidirectional model development in Verilog-A for photonic devices arXiv.cs.ET Pub Date : 2024-02-15 Dias Azhigulov, Zeqin Lu, James Pond, Lukas Chrostowski, Sudip Shekhar
We present a method to model photonic components in Verilog-A by introducing bidirectional signaling through a single port. To achieve this, the concept of power waves and scattering parameters from electromagnetism are employed. As a consequence, one can simultaneously transmit forward and backward propagating waves on a single wire while also capturing realistic, measurement-backed response of photonic
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A 3D Memristor Architecture for In-Memory Computing Demonstrated with SHA3 arXiv.cs.ET Pub Date : 2024-02-14 Muayad J. Aljafar, Rasika Joshi, John M. Acken
Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid CMOS-memristors technology demonstrated with SHA3 over implementations that use only memristor technology. In the proposed technique, SHA3 is implemented in a set of perpendicular
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Neuromorphic hardware for sustainable AI data centers arXiv.cs.ET Pub Date : 2024-02-04 Bernhard Vogginger, Amirhossein Rostami, Vaibhav Jain, Sirine Arfa, Andreas Hantsch, David Kappel, Michael Schäfer, Ulrike Faltings, Hector A. Gonzalez, Chen Liu, Christian Mayr
As humans advance toward a higher level of artificial intelligence, it is always at the cost of escalating computational resource consumption, which requires developing novel solutions to meet the exponential growth of AI computing demand. Neuromorphic hardware takes inspiration from how the brain processes information and promises energy-efficient computing of AI workloads. Despite its potential,
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Efficient Shape Formation by 3D Hybrid Programmable Matter: An Algorithm for Low Diameter Intermediate Structures arXiv.cs.ET Pub Date : 2024-01-31 Kristian Hinnenthal, David Liedtke, Christian Scheideler
This paper considers the shape formation problem within the 3D hybrid model, where a single agent with a strictly limited viewing range and the computational capacity of a deterministic finite automaton manipulates passive tiles through pick-up, movement, and placement actions. The goal is to reconfigure a set of tiles into a specific shape termed an icicle. The icicle, identified as a dense, hole-free
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Resource Bounds for Quantum Circuit Mapping via Quantum Circuit Complexity arXiv.cs.ET Pub Date : 2024-02-01 Matthew Steinberg, Medina Bandic, Sacha Szkudlarek, Carmen G. Almudever, Aritra Sarkar, Sebastian Feld
Efficiently mapping quantum circuits onto hardware is an integral part of the quantum compilation process, wherein a quantum circuit is modified in accordance with the stringent architectural demands of a quantum processor. Many techniques exist for solving the quantum circuit mapping problem, many of which relate quantum circuit mapping to classical computer science. This work considers a novel perspective
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Spin Wave Threshold Gate arXiv.cs.ET Pub Date : 2024-01-22 Arne Van Zegbroeck, Pantazis Anagnostou, Said Hamdioui, Christop Adelmann, Florin Ciubotaru, Sorin Cotofana
While Spin Waves (SW) interaction provides natural support for low power Majority (MAJ) gate implementations many hurdles still exists on the road towards the realization of practically relevant SW circuits. In this paper we leave the SW interaction avenue and propose Threshold Logic (TL) inspired SW computing, which relies on successive phase rotations applied to one single SW instead of on the interference
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Spintronic logic: from transducers to logic gates and circuits arXiv.cs.ET Pub Date : 2024-01-18 Christoph Adelmann, Florin Ciubotaru, Fanfan Meng, Sorin Cotofana, Sebastien Couet
While magnetic solid-state memory has found commercial applications to date, magnetic logic has rather remained on a conceptual level so far. Here, we discuss open challenges of different spintronic logic approaches, which use magnetic excitations for computation. While different logic gate designs have been proposed and proof of concept experiments have been reported, no nontrivial operational spintronic
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Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training arXiv.cs.ET Pub Date : 2024-01-18 Corey Lammie, Athanasios Vasilopoulos, Julian Büchel, Giacomo Camposampiero, Manuel Le Gallo, Malte Rasch, Abu Sebastian
Analog-Based In-Memory Computing (AIMC) inference accelerators can be used to efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate accuracy losses, due to circuit and device non-idealities, Hardware-Aware (HWA) training methodologies must be employed. These typically require significant information about the underlying hardware. In this paper, we propose two Post-Training
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LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles arXiv.cs.ET Pub Date : 2024-01-17 Corey Lammie, Flavio Ponzina, Yuxuan Wang, Joshua Klein, Marina Zapater, Irem Boybat, Abu Sebastian, Giovanni Ansaloni, David Atienza
When arranged in a crossbar configuration, resistive memory devices can be used to execute MVM, the most dominant operation of many ML algorithms, in constant time complexity. Nonetheless, when performing computations in the analog domain, novel challenges are introduced in terms of arithmetic precision and stochasticity, due to non-ideal circuit and device behaviour. Moreover, these non-idealities
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A Novel Optimization Algorithm for Buffer and Splitter Minimization in Phase-Skipping Adiabatic Quantum-Flux-Parametron Circuits arXiv.cs.ET Pub Date : 2024-01-14 Robert S. Aviles, Peter A. Beerel
Adiabatic Quantum-Flux-Parametron (AQFP) logic is a promising emerging device technology that promises six orders of magnitude lower power than CMOS. However, AQFP is challenged by operation at only ultra-low temperatures, has high latency and area, and requires a complex clocking scheme. In particular, every logic gate, buffer, and splitter must be clocked and each pair of connected clocked gates
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An Efficient and Scalable Clocking Assignment Algorithm for Multi-Threaded Multi-Phase Single Flux Quantum Circuits arXiv.cs.ET Pub Date : 2024-01-12 Robert S. Aviles, Xi Li, Lei Lu, Zhaorui Ni, Peter A. Beerel
A key distinguishing feature of single flux quantum (SFQ) circuits is that each logic gate is clocked. This feature forces the introduction of path-balancing flip-flops to ensure proper synchronization of inputs at each gate. This paper proposes a polynomial time complexity approximation algorithm for clocking assignments that minimizes the insertion of path balancing buffers for multi-threaded multi-phase
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FeReX: A Reconfigurable Design of Multi-bit Ferroelectric Compute-in-Memory for Nearest Neighbor Search arXiv.cs.ET Pub Date : 2024-01-11 Zhicheng Xu, Che-Kai Liu, Chao Li, Ruibin Mao, Jianyi Yang, Thomas Kämpfe, Mohsen Imani, Can Li, Cheng Zhuo, Xunzhao Yin
Rapid advancements in artificial intelligence have given rise to transformative models, profoundly impacting our lives. These models demand massive volumes of data to operate effectively, exacerbating the data-transfer bottleneck inherent in the conventional von-Neumann architecture. Compute-in-memory (CIM), a novel computing paradigm, tackles these issues by seamlessly embedding in-memory search functions
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SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning arXiv.cs.ET Pub Date : 2024-01-09 Hector A. Gonzalez, Jiaxin Huang, Florian Kelber, Khaleelulla Khan Nazeer, Tim Langer, Chen Liu, Matthias Lohrmann, Amirhossein Rostami, Mark Schöne, Bernhard Vogginger, Timo C. Wunderlich, Yexin Yan, Mahmoud Akl, Christian Mayr
The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This development is accompanied by a rapid growth of the required computational demands for larger models and more data. Concurrently, emerging properties of foundation models such as in-context learning drive new opportunities
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Hiding Information for Secure and Covert Data Storage in Commercial ReRAM Chips arXiv.cs.ET Pub Date : 2024-01-09 Farah Ferdaus, B. M. S. Bahar Talukder, Md Tauhidur Rahman
This article introduces a novel, low-cost technique for hiding data in commercially available resistive-RAM (ReRAM) chips. The data is kept hidden in ReRAM cells by manipulating its analog physical properties through switching ($\textit{set/reset}$) operations. This hidden data, later, is retrieved by sensing the changes in cells' physical properties (i.e., $\textit{set/reset}$ time of the memory cells)
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A New Dataflow Implementation to Improve Energy Efficiency of Monolithic 3D Systolic Arrays arXiv.cs.ET Pub Date : 2024-01-07 Prachi Shukla, Vasilis F. Pavlidis, Emre Salman, Ayse K. Coskun
Systolic arrays are popular for executing deep neural networks (DNNs) at the edge. Low latency and energy efficiency are key requirements in edge devices such as drones and autonomous vehicles. Monolithic 3D (MONO3D) is an emerging 3D integration technique that offers ultra-high bandwidth among processing and memory elements with a negligible area overhead. Such high bandwidth can help meet the ever-growing
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Quantum Leak: Timing Side-Channel Attacks on Cloud-Based Quantum Services arXiv.cs.ET Pub Date : 2024-01-03 Chao Lu, Esha Telang, Aydin Aysu, Kanad Basu
Quantum computing offers significant acceleration capabilities over its classical counterpart in various application domains. Consequently, there has been substantial focus on improving quantum computing capabilities. However, to date, the security implications of these quantum computing platforms have been largely overlooked. With the emergence of cloud-based quantum computing services, it is critical
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Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET arXiv.cs.ET Pub Date : 2023-12-29 Yifei Zhou, Xuchu Huang, Jianyi Yang, Kai Ni, Hussam Amrouch, Cheng Zhuo, Xunxhao Yin
Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as 'memory wall' issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low
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Inkjet-Printed High-Yield, Reconfigurable, and Recyclable Memristors on Paper arXiv.cs.ET Pub Date : 2023-12-27 Jinrui Chen, Mingfei Xiao, Zesheng Chen, Sibghah Khan, Saptarsi Ghosh, Nasiruddin Macadam, Zhuo Chen, Binghan Zhou, Guolin Yun, Kasia Wilk, Feng Tian, Simon Fairclough, Yang Xu, Rachel Oliver, Tawfique Hasan
Reconfigurable memristors featuring neural and synaptic functions hold great potential for neuromorphic circuits by simplifying system architecture, cutting power consumption, and boosting computational efficiency. Their additive manufacturing on sustainable substrates offers unique advantages for future electronics, including low environmental impact. Here, exploiting structure-property relationship
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Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications arXiv.cs.ET Pub Date : 2023-12-28 Hankyul Baek, Donghyeon Kim, Joongheon Kim
Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data. As convolutional neural networks (CNNs) are being optimized, the performances and computation speeds of object detection in autonomous driving have been significantly improved. However, due to the exponentially rapid
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Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning arXiv.cs.ET Pub Date : 2023-12-24 Bibhas Manna, Arnob Saha, Zhouhang Jiang, Kai Ni, Abhronil Sengupta
Reliability issues stemming from device level non-idealities of non-volatile emerging technologies like ferroelectric field-effect transistors (FeFET), especially at scaled dimensions, cause substantial degradation in the accuracy of In-Memory crossbar-based AI systems. In this work, we present a variation-aware design technique to characterize the device level variations and to mitigate their impact
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QAOA on Hamiltonian Cycle problem arXiv.cs.ET Pub Date : 2023-12-26 Zhuoyang Ye
I use QAOA to solve the Hamiltonian Circle problem. First, inspired by Lucas, I define the QUBO form of Hamiltonian Cycle and transform it to a quantum circuit by embedding the problem of $n$ vertices to an encoding of $(n-1)^2$ qubits. Then, I calcluate the spectrum of the cost hamiltonian for both triangle case and square case and justify my definition. I also write a python program to generate the
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Programmable electrical coupling between stochastic magnetic tunnel junctions arXiv.cs.ET Pub Date : 2023-12-20 Sidra Gibeault, Temitayo N. Adeyeye, Liam A. Pocher, Daniel P. Lathrop, Matthew W. Daniels, Mark D. Stiles, Jabez J. McClelland, William A. Borders, Jason T. Ryan, Philippe Talatchian, Ursula Ebels, Advait Madhavan
Superparamagnetic tunnel junctions (SMTJs) are promising sources of randomness for compact and energy efficient implementations of probabilistic computing techniques. Augmenting an SMTJ with electronic circuits, to convert the random telegraph fluctuations of its resistance state to stochastic digital signals, gives a basic building block known as a probabilistic bit or $p$-bit. Though scalable probabilistic
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VECOM: Variation Resilient Encoding and Offset Compensation Schemes for Reliable ReRAM Based DNN Accelerator arXiv.cs.ET Pub Date : 2023-12-18 Je-Woo Jang, Thai-Hoang Nguyen, Joon-Sung Yang
Resistive Random Access Memory (ReRAM) based Processing In Memory (PIM) Accelerator has emerged as a promising computing architecture for memory intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity, ReRAM devices often suffer from various reliability issues, which hinder the practicality of the PIM architecture and lead to a severe degradation in DNN accuracy
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Limits to the Energy Efficiency of CMOS Microprocessors arXiv.cs.ET Pub Date : 2023-12-14 Anson Ho, Ege Erdil, Tamay Besiroglu
CMOS microprocessors have achieved massive energy efficiency gains but may reach limits soon. This paper presents an approach to estimating the limits on the maximum floating point operations per Joule (FLOP/J) for CMOS microprocessors. We analyze the three primary sources of energy dissipation: transistor switching, interconnect capacitances and leakage power. Using first-principles calculations of
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Reservoir Computing with Colloidal Mixtures of ZnO and Proteinoids arXiv.cs.ET Pub Date : 2023-12-13 Raphael Fortulan, Noushin Raeisi Kheirabadi, Panagiotis Mougkogiannis, Alessandro Chiolerio, Andrew Adamatzky
Liquid computers use incompressible fluids for computational processes. Here we present experimental laboratory prototypes of liquid computers using colloids composed of zinc oxide (ZnO) nanoparticles and microspheres containing thermal proteins (proteinoids). The choice of proteinoids is based on their distinctive neuron-like electrical behaviour and their similarity to protocells. In addition, ZnO
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Toward Automated Quantum Variational Machine Learning arXiv.cs.ET Pub Date : 2023-12-04 Omer Subasi
In this work, we address the problem of automating quantum variational machine learning. We develop a multi-locality parallelizable search algorithm, called MUSE, to find the initial points and the sets of parameters that achieve the best performance for quantum variational circuit learning. Simulations with five real-world classification datasets indicate that on average, MUSE improves the detection
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Scaling-up Memristor Monte Carlo with magnetic domain-wall physics arXiv.cs.ET Pub Date : 2023-12-05 Thomas Dalgaty, Shogo Yamada, Anca Molnos, Eiji Kawasaki, Thomas Mesquida, François Rummens, Tatsuo Shibata, Yukihiro Urakawa, Yukio Terasaki, Tomoyuki Sasaki, Marc Duranton
By exploiting the intrinsic random nature of nanoscale devices, Memristor Monte Carlo (MMC) is a promising enabler of edge learning systems. However, due to multiple algorithmic and device-level limitations, existing demonstrations have been restricted to very small neural network models and datasets. We discuss these limitations, and describe how they can be overcome, by mapping the stochastic gradient
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Heisenberg machines with programmable spin-circuits arXiv.cs.ET Pub Date : 2023-12-03 Saleh Bunaiyan, Supriyo Datta, Kerem Y. Camsari
We show that we can harness two recent experimental developments to build a compact hardware emulator for the classical Heisenberg model in statistical physics. The first is the demonstration of spin-diffusion lengths in excess of microns in graphene even at room temperature. The second is the demonstration of low barrier magnets (LBMs) whose magnetization can fluctuate rapidly even at sub-nanosecond
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Performance Analysis of Multi-Angle QAOA for p > 1 arXiv.cs.ET Pub Date : 2023-11-30 Igor Gaidai, Rebekah Herrman
In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. However, MA-QAOA is not optimal for minimization of the total QPU time. Different optimization initialization strategies are considered and compared for both QAOA
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Provable bounds for noise-free expectation values computed from noisy samples arXiv.cs.ET Pub Date : 2023-12-01 Samantha V. Barron, Daniel J. Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner
In this paper, we explore the impact of noise on quantum computing, particularly focusing on the challenges when sampling bit strings from noisy quantum computers as well as the implications for optimization and machine learning applications. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the
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Skipper: Improving the Reach and Fidelity of Quantum Annealers by Skipping Long Chains arXiv.cs.ET Pub Date : 2023-12-01 Ramin Ayanzadeh, Moinuddin Qureshi
Quantum Annealers (QAs) operate as single-instruction machines, lacking a SWAP operation to overcome limited qubit connectivity. Consequently, multiple physical qubits are chained to form a program qubit with higher connectivity, resulting in a drastically diminished effective QA capacity by up to 33x. We observe that in QAs: (a) chain lengths exhibit a power-law distribution, a few dominant chains
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Efficient and Scalable Architecture for Multiple-chip Implementation of Simulated Bifurcation Machines arXiv.cs.ET Pub Date : 2023-11-29 Tomoya Kashimata, Masaya Yamasaki, Ryo Hidaka, Kosuke Tatsumura
Ising machines are specialized computers for finding the lowest energy states of Ising spin models, onto which many practical combinatorial optimization problems can be mapped. Simulated bifurcation (SB) is a quantum-inspired parallelizable algorithm for Ising problems that enables scalable multi-chip implementations of Ising machines. However, the computational performance of a previously proposed
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Scaling Whole-Chip QAOA for Higher-Order Ising Spin Glass Models on Heavy-Hex Graphs arXiv.cs.ET Pub Date : 2023-12-02 Elijah Pelofske, Andreas Bärtschi, Lukasz Cincio, John Golden, Stephan Eidenbenz
We show through numerical simulation that the Quantum Alternating Operator Ansatz (QAOA) for higher-order, random-coefficient, heavy-hex compatible spin glass Ising models has strong parameter concentration across problem sizes from $16$ up to $127$ qubits for $p=1$ up to $p=5$, which allows for straight-forward transfer learning of QAOA angles on instance sizes where exhaustive grid-search is prohibitive
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Neuromorphic Incremental on-chip Learning with Hebbian Weight Consolidation arXiv.cs.ET Pub Date : 2023-11-30 Zifan Ning, Chaojin Chen, Xiang Cheng, Wangzi Yao, Tielin Zhang, Bo Xu
As next-generation implantable brain-machine interfaces become pervasive on edge device, incrementally learning new tasks in bio-plasticity ways is urgently demanded for Neuromorphic chips. Due to the inherent characteristics of its structure, spiking neural networks are naturally well-suited for BMI-chips. Here we propose Hebbian Weight Consolidation, as well as an on-chip learning framework. HWC
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Odor Intensity Shift Keying (OISK) and Channel Capacity of Odor-based Molecular Communications in Internet of Everything arXiv.cs.ET Pub Date : 2023-11-30 Aditya Powari, Ozgur B. Akan
Molecular communication is a new, active area of research that has created a paradigm shift in the way a communication system is perceived. An artificial molecular communication network is created using biological molecules for encoding, transmitting and decoding the symbols to convey information. In addition to typical biological molecules, we are also exploring other classes of molecules that possess
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Towards Efficient Hyperdimensional Computing Using Photonics arXiv.cs.ET Pub Date : 2023-11-29 Farbin Fayza, Cansu Demirkiran, Hanning Chen, Che-Kai Liu, Avi Mohan, Hamza Errahmouni, Sanggeon Yun, Mohsen Imani, David Zhang, Darius Bunandar, Ajay Joshi
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs make it extremely challenging to design efficient silicon photonics-based systems for DNN inference and training. Hyperdimensional Computing (HDC) is an emerging
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65 GOPS/neuron Photonic Tensor Core with Thin-film Lithium Niobate Photonics arXiv.cs.ET Pub Date : 2023-11-28 Zhongjin Lin, Bhavin J. Shastri, Shangxuan Yu, Jingxiang Song, Yuntao Zhu, Arman Safarnejadian, Wangning Cai, Yanmei Lin, Wei Ke, Mustafa Hammood, Tianye Wang, Mengyue Xu, Zibo Zheng, Mohammed Al-Qadasi, Omid Esmaeeli, Mohamed Rahim, Grzegorz Pakulski, Jens Schmid, Pedro Barrios, Weihong Jiang, Hugh Morison, Matthew Mitchell, Xiaogang Qiang, Xun Guan, Nicolas A. F. Jaeger, Leslie A. n Rusch, Sudip
Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by providing low latency, high bandwidth, and energy-efficient computations. Here, we introduce a photonic tensor core processor enabled by time-multiplexed inputs and charge-integrated outputs. This fully integrated processor, comprising only two thin-film lithium niobate (TFLN) modulators, a III-V
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Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale arXiv.cs.ET Pub Date : 2023-11-27 Soyed Tuhin Ahmed, Kamal Danouchi, Michael Hefenbrock, Guillaume Prenat, Lorena Anghel, Mehdi B. Tahoori
Uncertainty estimation in Neural Networks (NNs) is vital in improving reliability and confidence in predictions, particularly in safety-critical applications. Bayesian Neural Networks (BayNNs) with Dropout as an approximation offer a systematic approach to quantifying uncertainty, but they inherently suffer from high hardware overhead in terms of power, memory, and computation. Thus, the applicability
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Localization of a Passive Source with a Sensor Network based Experimental Molecular Communication Platform arXiv.cs.ET Pub Date : 2023-11-28 Fatih Gulec, Damla Yagmur Koda, Baris Atakan, Andrew W. Eckford
In a practical molecular communication scenario such as monitoring air pollutants released from an unknown source, it is essential to estimate the location of the molecular transmitter (TX). This paper presents a novel Sensor Network-based Localization Algorithm (SNCLA) for passive transmission by using a novel experimental platform which mainly comprises a clustered sensor network (SN) with $24$ sensor
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DIAC: Design Exploration of Intermittent-Aware Computing Realizing Batteryless Systems arXiv.cs.ET Pub Date : 2023-11-28 Sepehr Tabrizchi, Shaahin Angizi, Arman Roohi
Battery-powered IoT devices face challenges like cost, maintenance, and environmental sustainability, prompting the emergence of batteryless energy-harvesting systems that harness ambient sources. However, their intermittent behavior can disrupt program execution and cause data loss, leading to unpredictable outcomes. Despite exhaustive studies employing conventional checkpoint methods and intricate
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DGR: Tackling Drifted and Correlated Noise in Quantum Error Correction via Decoding Graph Re-weighting arXiv.cs.ET Pub Date : 2023-11-27 Hanrui Wang, Pengyu Liu, Yilian Liu, Jiaqi Gu, Jonathan Baker, Frederic T. Chong, Song Han
Quantum hardware suffers from high error rates and noise, which makes directly running applications on them ineffective. Quantum Error Correction (QEC) is a critical technique towards fault tolerance which encodes the quantum information distributively in multiple data qubits and uses syndrome qubits to check parity. Minimum-Weight-Perfect-Matching (MWPM) is a popular QEC decoder that takes the syndromes
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Q-Pilot: Field Programmable Quantum Array Compilation with Flying Ancillas arXiv.cs.ET Pub Date : 2023-11-26 Hanrui Wang, Bochen Tan, Pengyu Liu, Yilian Liu, Jiaqi Gu, Jason Cong, Song Han
Neutral atom arrays have become a promising platform for quantum computing, especially the \textit{field programmable qubit array} (FPQA) endowed with the unique capability of atom movement. This feature allows dynamic alterations in qubit connectivity during runtime, which can reduce the cost of executing long-range gates and improve parallelism. However, this added flexibility introduces new challenges
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A Survey Examining Neuromorphic Architecture in Space and Challenges from Radiation arXiv.cs.ET Pub Date : 2023-11-25 Jonathan Naoukin, Murat Isik, Karn Tiwari
Inspired by the human brain's structure and function, neuromorphic computing has emerged as a promising approach for developing energy-efficient and powerful computing systems. Neuromorphic computing offers significant processing speed and power consumption advantages in aerospace applications. These two factors are crucial for real-time data analysis and decision-making. However, the harsh space environment
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Application of Reconfigurable All-Optical Activation Unit based on Optical Injection into Bistable Fabry-Pérot Laser in Multilayer Perceptron Neural Networks arXiv.cs.ET Pub Date : 2023-11-23 Jasna V. Crnjanski, Isidora Teofilović, Marko M. Krstić, Dejan M. Gvozdić
In this paper we theoretically investigate application of a bistable Fabry-P\'{e}rot semiconductor laser under optical-injection as all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition
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Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers arXiv.cs.ET Pub Date : 2023-11-27 Hanrui Wang, Pengyu Liu, Kevin Shao, Dantong Li, Jiaqi Gu, David Z. Pan, Yongshan Ding, Song Han
Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable limits for useful algorithm execution. Quantum Error Correction (QEC) mitigates this by employing redundancy, distributing quantum information across multiple data qubits and utilizing syndrome qubits to monitor their states