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Big data processing has exposed the limits of compute-centric hardware acceleration due to the memory-to-processor bandwidth bottleneck. Consequently, there has been a shift towards memory-centric architectures, leveraging substantial compute parallelism by processing using the memory elements directly. Computing-in-memory (CIM) proposals for both conventional and emerging memory technologies often target massively parallel operations. However, current CIM solutions face significant challenges. For emerging data-intensive applications, such as advanced machine learning techniques and bioinformatics, where matrix multiplication is a key primitive, memristor crossbars suffer from limited write endurance and expensive write operations. In contrast, while DRAM-based solutions have successfully demonstrated multiplication using additions, they remain prohibitively slow. This paper introduces Count2Multiply, a technology-agnostic digital-CIM method for performing integer-binary and integer-integer matrix multiplications using high-radix, massively parallel counting implemented with bitwise logic operations. In addition, Count2Multiply is designed with fault tolerance in mind and leverages traditional scalable row-wise error correction codes, such as Hamming and BCH codes, to protect against the high error rates of existing CIM designs. We demonstrate Count2Multiply with a detailed application to CIM in conventional DRAM due to its ubiquity and high endurance. We also explore the acceleration potential of racetrack memories due to their shifting properties, which are natural for Count2Multiply, and their high endurance. Compared to the state-of-the-art in-DRAM method, Count2Multiply achieves up to 10x speedup, 3.8x higher GOPS/Watt, and 1.4x higher GOPS/area, while the RTM counterpart offers gains of 10x, 57x, and 3.8x., Comment: 14 pages

Graph Isomorphism (GI) is a fundamental algorithmic problem. Amongst graph classes for which the computational complexity of GI has been resolved, trees are arguably the most fundamental. Tree Isomorphism is complete for deterministic logspace, a tiny subclass of polynomial time, by Lindell's result. Over three decades ago, he devised a deterministic logspace algorithm that computes a string which is a canon for the input tree -- two trees are isomorphic precisely when their canons are identical. Inspired by Miller-Reif's reduction of Tree Isomorphism to Polynomial Identity Testing, we present a new logspace algorithm for tree canonization fundamentally different from Lindell's algorithm. Our algorithm computes a univariate polynomial as canon for an input tree, based on the classical Eisenstein's criterion for the irreducibility of univariate polynomials. This can be implemented in logspace by invoking the well known Buss et al. algorithm for arithmetic formula evaluation. This algorithm is conceptually very simple, avoiding the delicate case analysis and complex recursion that constitute the core of Lindell's algorithm. We illustrate the adaptability of our algorithm by extending it to a couple of other classes of graphs.

This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

Computing-in-memory (CIM) promises to alleviate the Von Neumann bottleneck and accelerate data-intensive applications. Depending on the underlying technology and configuration, CIM enables implementing compute primitives in place, such as multiplication, search operations, and bulk bitwise logic operations. Emerging nonvolatile memory technologies such as spintronic Racetrack memory (RTM) promise not only unprecedented density but also significant parallelism through CIM. However, most CIM designs, including those based on RTM, exhibit high fault rates. Existing error correction codes (ECC) are not homomorphic over bitwise operations such as AND and OR, and hence cannot protect against CIM faults. This paper proposes CIRM-ECC, a technique to protect spintronic RTMs against CIM faults. At the core of CIRM-ECC, we use a recently proposed RTM-based CIM approach and leverage its peripheral circuitry to our implement our novel ECC codes. We show that CIRM-ECC can be applied to single-bit Hamming codes as well as multi-bit BCH codes., Comment: 4 pages, 6 figures, to be submitted to IEEE CAL

Which dynamic queries can be maintained efficiently? For constant-size changes, it is known that constant-depth circuits or, equivalently, first-order updates suffice for maintaining many important queries, among them reachability, tree isomorphism, and the word problem for context-free languages. In other words, these queries are in the dynamic complexity class DynFO. We show that most of the existing results for constant-size changes can be recovered for batch changes of polylogarithmic size if one allows circuits of depth O(log log n) or, equivalently, first-order updates that are iterated O(log log n) times., Comment: Full version of an MFCS 2024 paper

Ferroelectricity is characterized by the presence of spontaneous and switchable macroscopic polarization. Scaling limits of ferroelectricity have been of both fundamental and technological importance, but the probes of ferroelectricity have often been indirect due to confounding factors such as leakage in the direct electrical measurements. Recent interest in low-voltage switching electronic devices squarely puts the focus on ultrathin limits of ferroelectricity in an electronic device form, specifically on the robustness of ferroelectric characteristics such as retention and endurance for practical applications. Here, we illustrate how manipulating the kinetic energy of the plasma plume during pulsed laser deposition can yield ultrathin ferroelectric capacitor heterostructures with high bulk and interface quality, significantly low leakage currents and a broad "growth window". These heterostructures venture into previously unexplored aspects of ferroelectric properties, showcasing ultralow switching voltages ($<$0.3 V), long retention times ($>$10$^{4}$s), and high endurance ($>$10$^{11}$cycles) in 20 nm films of the prototypical perovskite ferroelectric, BaTiO$_{3}$. Our work demonstrates that materials engineering can push the envelope of performance for ferroelectric materials and devices at the ultrathin limit and opens a direct, reliable and scalable pathway to practical applications of ferroelectrics in ultralow voltage switches for logic and memory technologies.

High voltage (~2 kV) AlGaN-channel HEMTs were fabricated with 64% Aluminum composition in the channel. The average on-resistance was ~75 ohm. mm (~21 miliohm. cm^2) for LGD = 20 microns. Breakdown voltage reached >3 kV (tool limit) before passivation however it reduced to ~2 kV after SiN surface passivation and field plates. The apparent high breakdown voltage prior to passivation can possibly be attributed to the field plate effect of the charged trap states of the surface. The breakdown voltage and RON demonstrated a strong linear correlation in a scattered plot with ~50 measured transistors. In pulsed IV measurements with 100 microsecond pulse width and 40 V of off-state bias (tool limit), the dynamic RON increased by ~5% compared to DC RON and current collapse was <10%., Comment: 4 pages, 6 figures

In real-time status update services for the Internet of Things (IoT), the timely dissemination of information requiring timely updates is crucial to maintaining its relevance. Failing to keep up with these updates results in outdated information. The age of information (AoI) serves as a metric to quantify the freshness of information. The Existing works to optimize AoI primarily focus on the transmission time from the information source to the monitor, neglecting the transmission time from the monitor to the destination. This oversight significantly impacts information freshness and subsequently affects decision-making accuracy. To address this gap, we designed an edge-enabled vehicular fog system to lighten the computational burden on IoT devices. We examined how information transmission and request-response times influence end-to-end AoI. As a solution, we proposed Dueling-Deep Queue Network (dueling-DQN), a deep reinforcement learning (DRL)-based algorithm and compared its performance with DQN policy and analytical results. Our simulation results demonstrate that the proposed dueling-DQN algorithm outperforms both DQN and analytical methods, highlighting its effectiveness in improving real-time system information freshness. Considering the complete end-to-end transmission process, our optimization approach can improve decision-making performance and overall system efficiency.

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions.

This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field., Comment: NTIRE 2024 webpage: https://cvlai.net/ntire/2024. Code: https://github.com/zhengchen1999/NTIRE2024_ImageSR_x4

This paper introduces a vision for Quantum Software Development lifecycle, proposing a hybrid full-stack iterative model that integrates quantum and classical computing. Addressing the current challenges in Quantum Computing (QC) such as the need for integrating diverse programming languages and managing the complexities of quantum-classical systems, this model is rooted in the principles of DevOps and continuous software engineering. It presents a comprehensive lifecycle for quantum software development, encompassing quantum-agnostic coding, testing, deployment, cloud computing services, orchestration, translation, execution, and interpretation phases. Each phase is designed to accommodate the unique demands of QC, enabling traditional software developers to engage with QC environments without needing in-depth QC expertise. The paper presents a detailed implementation roadmap, utilizing a range of existing tools and frameworks, thereby making quantum software development more accessible and efficient. The proposed model not only addresses current challenges in quantum software development but also makes a substantial contribution to the field of Quantum Software Engineering (QSE). By proposing a structured and accessible model, it sets the stage for further advancements and research in QSE, enhancing its practicality and relevance in a wide range of applications.

Mesoporous silica particles are promising candidates for drug delivery applications. In this paper, we first synthesize mesoporous silica MCM-41 and its derivative MCM-41GA with anchored glutaraldehyde bridges, and characterize them using a variety of techniques, including nitrogen adsorption/desorption, X-ray diffraction, NMR spectroscopy, scanning electron microscopy, and thermogravimetric analysis. Then, we employ random optical fields to sort mesoporous silica particles. Random optical fields by containing local intensity gradients throughout a wide range of field of view provide an elegant, easy-to-implement, and low-cost variant of multiple optical tweezers, which is known as speckle tweezers (ST). ST, similar to multiple optical tweezers, for manipulation tasks, such as trapping, sorting, and guiding of collection of micro and sub-micro objects in several disciplines including statistical physics, chemistry, microfluidics and material science. We show that ST can restrict, sieve, and sort MCM-41 and MCM-41GA particles. The different interaction of mesoporous silica variations with the applied ST may be attributed to the pre-applied modification and the differences in the porosity structure and distribution. Therefore, the results provide insight into the textural and chemical characteristics of mesoporous materials, contributing to a deeper understanding of their potential applications.

In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of time and energy presents a formidable challenge. Conventional computing systems and accelerators are continually being pushed to their limits to stay competitive. In this context, computing near-memory (CNM) and computing-in-memory (CIM) have emerged as potentially game-changing paradigms. This survey introduces the basics of CNM and CIM architectures, including their underlying technologies and working principles. We focus particularly on CIM and CNM architectures that have either been prototyped or commercialized. While surveying the evolving CIM and CNM landscape in academia and industry, we discuss the potential benefits in terms of performance, energy, and cost, along with the challenges associated with these cutting-edge computing paradigms.

The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy demands and latency in von Neumann systems. Several computing-in-memory (CIM) systems have recently been proposed to overcome this, but trade-offs involving accuracy, hardware reliability, and scalability for large models remain a challenge. Additionally, for some CIM designs, the activation movement still requires considerable time and energy. This paper explores the combination of algorithmic optimizations for ternary weight neural networks and associative processors (APs) implemented using racetrack memory (RTM). We propose a novel compilation flow to optimize convolutions on APs by reducing their arithmetic intensity. By leveraging the benefits of RTM-based APs, this approach substantially reduces data transfers within the memory while addressing accuracy, energy efficiency, and reliability concerns. Concretely, our solution improves the energy efficiency of ResNet-18 inference on ImageNet by 7.5x compared to crossbar in-memory accelerators while retaining software accuracy., Comment: To be presented at DATE24