main webpage W Topic Rectifier (Neural Networks) IEEE Journal on Exploratory Solid-State Computational Devices and Circuits • Vol 11 1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations 2025 Ternary weight neural networks (TWNs), with weights quantized to three states (&#x2212;1, 0, and 1), have emerged as promising solutions for resource-constrained edge artificial intelligence (AI) platforms due to their high energy efficiency with acceptable i… Article Open Rectifier (Neural Networks) Open Article Page

Rectifier (Neural Networks)

Activation function In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the positive part of its argument: where x is the input to a neuron. This is also known as a ramp function and is analogous to half-wave rectification in electrical engineering. This activation function was introduced by Kunihiko Fukushima in 1969 in the context of visual feature extraction in hierarchical neural networks. It was later argued that it has strong biological motivations and mathematical justifications. In 2011 it was found to enable better training of deeper networks, compared to the widely used activation functions prior to 2011, e.g. Critical Symbolic Virtual Narrative Open Rectifier (Neural Networks) Open Article Page

Exploring foci of: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits • Vol 11 1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations 2025 Ternary weight neural networks (TWNs), with weights quantized to three states (&#x2212;1, 0, and 1), have emerged as promising solutions for resource-constrained edge artificial intelligence (AI) platforms due to their high energy efficiency with acceptable inference accuracy. Further energy savings can be achieved with TWN accelerators utilizing techniques such as compute-in-memory (CiM) and scalable technologies such as ferroelectric transistors (FeFETs). Although the standard 1T-FeFET CiM design offers high den… Open Article Page

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Explore Rectifier (Neural Networks) W Topic Rectifier (Neural Networks) IEEE Journal on Exploratory Solid-State Computational Devices and Circuits • Vol 11 1.58-b FeFET-Based Ternary Neural Networks: Achieving Robust Compute-In-Memory With Weight-Input Transformations 2025 Ternary weight neural networks (TWNs), with weights quantized to three states (&#x2212;1, 0, and 1), have emerged as promising solutions for resource-constrained edge artificial intelligence (AI) platforms due to their high energy efficiency with acceptable i… Article Critical Symbolic Virtual Narrative Open Rectifier (Neural Networks) Open Article Page