8,858 Results Found

Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory proce...

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data...

Processing-in-memory and in-memory computing (PIM/IMC) are increasingly explored to mitigate the von Neumann data-movement bottleneck that limits deep neural network (DNN) performance and energy efficiency. Progress, however, remains fragmented acros...

As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based acce...

Processing-in-Memory (PIM) architectures are transforming system hierarchies by embedding computation within memory and collapsing traditional CPU–memory separation. This co-location enables measured gains in throughput and energy efficiency fo...

As a particular kind of detection technology under weak magnetization, metal magnetic memory testing is very likely to be affected by external factors in the detecting process, which may lead to incorrect results. In order to minimize the negative in...

As the scope of artificial intelligence (AI) expands and the structure becomes more complex, the amount of data for inference and training has increased. In traditional computer architectures, the memory bandwidth limitations have intensified bottlen...

With the rapid development of 5G, artificial intelligence (AI), and high-performance computing (HPC), there is a huge increase in the data exchanged between the processor and memory. However, the “storage wall” caused by the von Neumann a...

Memory is the biggest limiting factor to the widespread use of FPGAs for high-level image processing, which require complete frame(s) to be stored in situ. Since FPGAs have limited on-chip memory capabilities, efficient use of such resources is essen...

The range-Doppler algorithm (RDA), a key technique for generating synthetic aperture radar (SAR) images, offers high-resolution images but requires significant memory resources and involves complex signal processing. Moreover, the multitude of fast F...

DRAM-based in-memory computing integrates computational regions into the main memory, enabling local data processing within the memory, thereby achieving faster and more efficient data computation. However, enhancing system performance requires addre...

Image processing systems are widely used in space applications, so different radiation-induced malfunctions may occur in the system depending on the device that is implementing the algorithm. SRAM-based FPGAs are commonly used to speed up the image p...

A lot of research on deep learning and big data has led to efficient methods for processing large volumes of data and research on conserving computing resources. Particularly in domains like the IoT (Internet of Things), where the computing power is...

Language influences how we process information from multiple domains. Thus, working in first (L1) or second language (L2) can modulate bilinguals’ performance on basic activities, such as visual search, decision-making, or reading. However, few...

The widespread applications of the wireless Internet of Things (IoT) is one of the leading factors in the emerging of Big Data. Huge amounts of data need to be transferred and processed. The bandwidth and latency of data transfers have posed a new ch...

Processing-in-Memory (PiM), which combines a memory device with a Processing Unit (PU) into an integrated chip, has drawn special attention in the field of Artificial Intelligence semiconductors. Currently, in the development and commercialization of...

This comprehensive review explores the advancements in processing-in-memory (PIM) techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses the challenges of monolithic chip architectures and highlights the benefits of...

Currently, Kronecker Matrix–Matrix Multiplication play a crucial role in many advanced applications across science and engineering, such as Quantum Computing (Tensor Representation of Quantum States, Quantum Gate Construction), Machine Learning...

Multisensory processing has long been recognized to enhance perception, cognition, and actions in adults. However, there is currently limited understanding of how multisensory stimuli, in comparison to unisensory stimuli, contribute to the developmen...

Arithmetic estimation requires maintaining numerical information while applying rule-based procedures for approximate calculation, yet it remains unclear whether such estimation strategy processing depends on the working memory state in which task-re...

In this work, we present an efficient Processing in MRAM-Accelerated De Bruijn Graph-based DNA Assembly platform, named PANDA, based on an optimized and hardware-friendly genome assembly algorithm. PANDA is able to assemble large-scale DNA sequence d...

This study investigates the effects of task-essential training on offline and online processing of verbal morphology and explores how working memory (WM) modulates the effects of training. We compare a no-training control group to two training groups...

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI a...

Although cognitive abilities have been shown to facilitate multisensory processing in adults, the development of cognitive abilities such as working memory and intelligence, and their relationship to multisensory motor reaction times (MRTs), has not...

This paper presents a pseudo-static gain cell (PS-GC) with extended retention time for an embedded dynamic random-access memory (eDRAM) macro for analog processing-in-memory (PIM). The proposed eDRAM cell consists of a two-transistor (2T) gain cell w...

There is a paucity of reports examining the relationship between the integrity of the corpus callosum (CC) and different aspects of cognitive functioning in patients with first-episode (FES) and chronic schizophrenia (CS) simultaneously; furthermore,...

Content Addressable Memories (CAMs) are pivotal in high-speed packet processing systems, enabling rapid data lookup operations essential for applications such as routing, switching, and network security. While traditional Register-Transfer Level (RTL...

The processing map of Ni₄₇Ti₄₄Nb₉ (at %) shape memory alloy (SMA), which possesses B2 austenite phases and β-Nb phases at room temperature, is established in order to optimize the hot working parameters. Based on true stress-strain curves of NiTiNb S...

Working memory (WM) includes short-term storage and executive processing of information. WM has been suggested to be one of the key concepts to explain individual differences in fluid intelligence (Gf). However, only a few studies have investigated t...

The effect of thermomechanical processing (TMP) parameters on the structural-phase evolution and properties of Ni-enriched Ti–50.8 at.% Ni shape memory alloy was investigated. Compression deformation was performed at T = 300–500 °C, a...

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical...

This paper presents a synaptic driving circuit design for processing in-memory (PIM) hardware with a thin-film transistor (TFT) embedded flash (eFlash) for a binary/ternary-weight neural network (NN). An eFlash-based synaptic cell capable of programm...

This paper introduces an n-type pseudo-static gain cell (PS-nGC) embedded within dynamic random-access memory (eDRAM) for high-speed processing-in-memory (PIM) applications. The PS-nGC leverages a two-transistor (2T) gain cell and employs an n-type p...

Several studies demonstrated the association of hearing disorders with neurocognitive deficits and dementia disorders, but little is known about the effects of auditory rehabilitation on the cognitive performance of the elderly. Therefore, the resear...

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector...

Scratchpad memories have been shown to reduce power consumption, but the different characteristics of nanometer scale processes, such as increased leakage power, motivate an examination of how the benefits of these memories change with process scalin...

Objectives: To investigate whether a Multimodal Rehabilitation Program (MRP) affects the change in visual–spatial abilities, especially attention, information-processing speed, visual–spatial learning, the severity of depression, and stra...

Long-memory models are frequently used in finance and other fields to capture long-range dependence in time series data. However, correctly identifying whether a process has long memory is crucial. This paper highlights a significant limitation in us...

There is increasing evidence that even mild gestational iodine deficiency (GID) results in adverse neurocognitive impacts on offspring. It’s unclear, however, if these persist long-term and whether they can be ameliorated by iodine sufficiency in chi...

Genetic risk for schizophrenia has a negative impact on memory and other cognitive abilities in unaffected individuals, and it was recently shown that this effect is specific to males. Using functional MRI, we investigated the effect of a polygenic r...

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes....

In Italy, an increasing number of historical buildings have been abandoned in the last decades. As a response, some local administrations decided to foster renovation projects to preserve their heritage for future generations. Among them, the norther...

Working memory capacity (WMC) and fluid intelligence (Gf) are highly correlated, but what accounts for this relationship remains elusive. Process-overlap theory (POT) proposes that the positive manifold is mainly caused by the overlap of domain-gener...

Neural-networks (NNs) for the current feature analysis bring novel electrical safety functions in smart circuit breakers (CBs), especially for preventing the fire hazard from electric vehicle/bike battery charging. In this work, the edge artificial i...

Using the prime–probe comparison paradigm, Jacob, Breitmeyer, and Treviño (2013) demonstrated that information processing in visual short-term memory (VSTM) proceeds through three stages: sensory visible persistence (SVP), nonvisible informational pe...

In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by in...

Processing in Memory based on memristors is considered the most effective solution to overcome the Von Neumann bottleneck issue and has become a hot research topic. The execution efficiency of logical computation and in-memory data transmission is cr...

Breast cancer (BC) in younger age is a critical and potentially traumatic experience that can interrupt the continuity of self-narrative during a crucial phase. In the Narrative Identity framework the translation of memories into autobiographical nar...

We study the cognitive processing of visual working memory in three different conditions of memory load and configuration change. Altering this features has been shown to alter the brain’s processing in memory tasks. Most studies dealing with t...

Processing-in-Memory (PIM) based on Resistive Random Access Memory (RRAM) is an emerging acceleration architecture for artificial neural networks. This paper proposes an RRAM PIM accelerator architecture that does not use Analog-to-Digital Converters...