相关论文: Controllable reset behavior in domain wall-magneti…

The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks. However, the LIF neuron is still relatively simple compared to that in the biological brain.…

Batching has a fundamental influence on the efficiency of deep neural network (DNN) execution. However, for dynamic DNNs, efficient batching is particularly challenging as the dataflow graph varies per input instance. As a result,…

This paper proposes a spintronic neuron structure composed of a heterostructure of magnets and a piezoelectric with a magnetic tunnel junction (MTJ). The operation of the device is simulated using SPICE models. Simulation results illustrate…

Recognizing atypical mitotic figures in histopathology images allows physicians to correctly assess tumor aggressiveness. Although machine learning models could be exploited for automatically performing such a task, under domain shift these…

Neuromorphic computing (NC) architecture has shown its suitability for energy-efficient computation. Amongst several systems, spin-orbit torque (SOT) based domain wall (DW) devices are one of the most energy-efficient contenders for NC. To…

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio…

Understanding how the brain learns to compute functions reliably, efficiently and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could…

Spintronic-based brain-inspired neuromorphic computing has recently attracted significant attention due to the exceptional properties of magnetic microstructures, including nanoscale dimensions, high stability, and low energy consumption.…

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking…

Electroencephalography (EEG) headsets are the most commonly used sensing devices for Brain-Computer Interface. In real-world applications, there are advantages to extrapolating data from one user session to another. However, these…

Neuromorphic computing is an emerging technology enabling low-latency and energy-efficient signal processing. A key algorithmic tool in neuromorphic computing is spiking neural networks (SNNs). SNNs are biologically inspired neural networks…

Neuromorphic computing (NC) is considered as a potential vehicle for implementing energy-efficient artificial intelligence (AI). To realize NC, several materials systems are being investigated. Among them, the spin-orbit torque (SOT)…

Voltage-induced dynamic switching in magnetic tunnel junctions (MTJs) is a writing technique for voltage-controlled magnetoresistive random access memory (VCMRAM), which is expected to be an ultimate non-volatile memory with ultra-low power…

Magnetic domain walls are information tokens in both logic and memory devices, and hold particular interest in applications such as neuromorphic accelerators that combine logic in memory. Here, we show that devices based on the electrical…

A current research trend in neurocomputing involves the design of novel artificial neural networks incorporating the concept of time into their operating model. In this paper, a novel architecture that employs stigmergy is proposed.…

Domain-wall memory (DWM) has SRAM class access performance, low energy, high endurance, high density, and CMOS compatibility. Recently, shift reliability and processing-using-memory (PuM) proposals developed a need to count the number of…

Spintronic-based neuromorphic hardware offers high-density and rapid data processing at nanoscale lengths by leveraging magnetic configurations like skyrmion and domain walls. Here, we present the maximal hardware implementation of a…

Voltage-controlled magnetoresistive random access memory (VC-MRAM) based on voltage-induced dynamic switching in magnetic tunnel junctions (MTJs) is a promising ultimate non-volatile memory with ultralow power consumption. However, the…

Non-volatile memristors offer a salient platform for artificial neural network (ANN), but the integration of different function blocks into one hardware system remains challenging. Here we demonstrate the implementation of brain-like…

Neural networks have achieved remarkable success in time series classification, but their reliance on large amounts of labeled data for training limits their applicability in cold-start scenarios. Moreover, they lack interpretability,…