A new technical paper titled “APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors Using DNN Learning” was published by researchers at UT Austin and Analog Devices. “Analog ...

The electronics design industry is changing quite a bit, driven mainly by the proliferation of sensors and demand to generate and gather more information. That’s leading to having more sensors, and ...

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Could analog artificial intelligence (AI) ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.

As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up. An ...

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of ...

“Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and ...

This article is part of our coverage of the latest in AI research. A new machine learning technique developed by researchers at Edge Impulse, a platform for creating ML models for the edge, makes it ...