Neuromorphic Chips and 'Brain‑Like' Computing: How Next‑Gen Chips May Change Phones, Robots, and IoT Devices

Explore how neuromorphic chips and brain-inspired computing bring low-power, efficient intelligence to edge AI, robotics, and ...
Semiconductor Engineering

Benchmarking Highly Parallel Hardware for Spiking Neural Networks in Robotics

Brain-inspired Spiking Neural Networks (SNN) and the parallel hardware necessary to exploit their full potential have promising features for robotic application. “Animal brains still outperform even ...
Electronic Design

Neuromorphic NPU Sips Power to Handle Edge Machine-Learning Models

What are spiking neural networks (SNNs)? Why the Akida Pico neural processing unit (NPU) can use so little power to handle machine-learning models. Why neuromorphic computing is important to ...
eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...