News
Neural networks made from photonic chips can be trained using on-chip backpropagation – the most widely used approach to training neural networks, according to a new study. The findings pave the way ...
A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...
In the previous chapter, we learned various strategies to guide AI models 'down the mountain' (optimization algorithms), such ...
An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...
The hype over Large Language Models (LLMs) has reached a fever pitch. But how much of the hype is justified? We can't answer that without some straight talk - and some definitions. Time for a ...
For more than eighty years, deep learning has relied on a simplified model of brain function. Now, a Pittsburgh startup ...
On September 15, People's Financial News reported that according to the Qichacha APP, the patent for "A Quantization Training Method, Device, and Equipment for a Neural Network Model" by Cambricon's ...
AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling proactive system optimization and enhanced performance.
Results that may be inaccessible to you are currently showing.
Hide inaccessible results
Feedback