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Researchers have developed neuromorphic hardware for artificial intelligence (AI) that uses very little power.

Cornell researchers create efficient chip architecture to lower energy consumption in AI and other technologies.

Innovative methods make artificial intelligence work efficiently on tiny sensors with limited power and memory.

Researchers propose brain-like spiking neural networks and compute-in-memory hardware as the key to overcoming the massive energy bottleneck stifling sustainable AI advancement.

Stable optical frequency combs generated via hyperparametric solitons could improve efficiency in high-speed communications.

New platforms bring powerful, energy-efficient AI processing to satellites and orbital data centers, enabling real-time decisions and faster analysis of Earth imagery.

MIT researchers have devised a system to make AI models work better by using data redundancy and developed a code generator.

Researchers embed AI models in radio waves to make edge computing more efficient and save energy on tiny gadgets.

Nosana brings decentralized AI computing to the forefront. Discover how this Solana-based project could change GPU access, making AI development more affordable and scalable for the Web3 future.

Join Alex Goh as we discuss EMC Protocol's technology for distributing AI workloads across diverse computing resources. How to provide transparent and secure ways to scale AI sustainably?