Reducing AI’s Carbon Footprint
Pursuing more efficient AI
News & features
Improve edge-device AI efficiency
Machine learning models are increasingly running on edge hardware, such as mobile phones or Internet of Things (IoT) devices. Motivations include protection of private data and avoidance of networking latency, for example with applications that recognize speech. Ensuring efficient inference…
Emit less carbon from AI
Multiple activities are involved in developing and using machine learning models, including selection of model architectures and algorithms, hyperparameter tuning, training on existing datasets, and making predictions on new data (aka inference). Optimizing results across these activities involves many complex…
Empower AI developers
Progress in machine learning is measured in part through the constant improvement of performance metrics such as accuracy or latency. Carbon footprint metrics, while being an equally important target, have not received the same degree of attention. With contributions from…
µTransfer: A technique for hyperparameter tuning of enormous neural networks
| Edward Hu, Greg Yang, and Jianfeng Gao
Great scientific achievements cannot be made by trial and error alone. Every launch in the space program is underpinned by centuries of fundamental research in aerodynamics, propulsion, and celestial bodies. In the same way, when it comes to building large-scale…
Factorized layers revisited: Compressing deep networks without playing the lottery
| Misha Khodak, Neil Tenenholtz, Lester Mackey, and Nicolo Fusi
From BiT (928 million parameters (opens in new tab)) to GPT-3 (175 billion parameters (opens in new tab)), state-of-the-art machine learning models are rapidly growing in size. With the greater expressivity and easier trainability of these models come skyrocketing training…
Archai can design your neural network with state-of-the-art neural architecture search (NAS)
| Shital Shah and Debadeepta Dey
The goal of neural architecture search (NAS) (opens in new tab) is to have computers automatically search for the best-performing neural networks. Recent advances in NAS methods have made it possible to build problem-specific networks that are faster, more compact,…