main webpage W Topic Neural Tube 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 Neural Tube Open Article Page

Neural Tube

Developmental precursor to the central nervous system In the developing chordate (including vertebrates), the neural tube is the embryonic precursor to the central nervous system, which is made up of the brain and spinal cord. The neural groove gradually deepens as the neural fold become elevated, and ultimately the folds meet and coalesce in the middle line and convert the groove into the closed neural tube. In humans, neural tube closure usually occurs by the fourth week of pregnancy (the 28th day after conception). … Critical Symbolic Virtual Narrative Open Neural Tube 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 Neural Tube W Topic Neural Tube 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 Neural Tube Open Article Page