Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture. The proposed approach leverages the three-terminal structure of domain-wall magnetic tunnel junction devices to establish a feedback loop that serves to train such devices when they are used as synapses in a neuromorphic computing architecture.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringSimilar Papers 제목 키워드 기반
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses binary STT-RAM devices as synapses and di…
Activity RecognitionHuman Activity RecognitionVocal Bursts Intensity PredictionImage Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network
The computational requirements of generative adversarial networks (GANs) exceed the limit of conventional Von Neumann architectures, necessitating energy efficient alternatives such as neuromorphic spintronics. This work…
Image GenerationSpatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation
Recently, machine learning systems have gained prominence in real-time, critical decision-making domains, such as autonomous driving and industrial automation. Their implementations should avoid overconfident predictions…
Autonomous DrivingDecision MakingTraining a multilayer dynamical spintronic network with standard machine learning tools to perform time series classification
The ability to process time-series at low energy cost is critical for many applications. Recurrent neural network, which can perform such tasks, are computationally expensive when implementing in software on conventional…
Time SeriesTime Series ClassificationA perspective on physical reservoir computing with nanomagnetic devices
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy r…