Скачать или смотреть On-Chip Trainable Spiking Neural Network Using CMOS Memristive STDP Synapses 🧠

Explore the circuit-level implementation of an on-chip trainable Spiking Neural Network (SNN) using CMOS-based memristive STDP synapses and LIF neurons in this in-depth technical video 🧠⚡.
This work bridges neuromorphic computing and VLSI design, demonstrating how biologically inspired learning rules like Spike-Timing Dependent Plasticity (STDP) can be efficiently realized in hardware.

You’ll gain insights into the memristor-based synapse design, CMOS compatibility, and how Leaky Integrate-and-Fire (LIF) neurons enable low-power, scalable neural computation. This approach is ideal for edge AI, brain-inspired hardware, and next-generation AI accelerators 🚀.

Whether you’re a researcher, PhD scholar, or VLSI/AI enthusiast, this video will help you understand how learning-enabled neural systems can be implemented directly on silicon 🔬.

👉 Like 👍 | Share 🔁 | Subscribe 🔔 for more advanced research content on neuromorphic engineering, AI hardware, and VLSI systems.

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Spiking Neural Network, Neuromorphic Computing, Memristive STDP, CMOS AI Hardware

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#CMOSDesign #VLSI #AIHardware #BrainInspiredComputing
#EdgeAI #LIFNeuron #OnChipLearning #Research #PhD
#ArtificialIntelligence #DeepTech #Semiconductor #takeoffedugroup #takeoffprojects