Hardware

TinyML shows promise for bringing deep learning to applications where electrical power is scarce, processing in the cloud is impractical, and/or data privacy is paramount.

Chinese companies have found loopholes to sidestep United States limits on AI chips. Facing severe limits on U.S. exports of high-performance chips, Chinese AI firms are purchasing them through subsidiaries and using them through cloud services, the Financial Times reported.

A new computing cluster delivers more bang per chip. Cerebras unveiled Andromeda, a supercomputer based on its processors. Unlike conventional clusters, the system’s processing speed rises linearly with additional processors.

Advanced AI requires advanced hardware. What if the global supply of high-end AI chips dries up? Most of the world’s advanced AI processors are manufactured in Taiwan, where tension with mainland China is rising.

Is your colossal text generator bogged down in training? Nvidia announced a chip designed to accelerate the transformer architecture, the basis of large language models such as GPT-3.

Facebook’s parent company is staking its future on a new compute cluster. Meta unveiled AI Research SuperCluster (RSC), which is designed to accelerate training of large models for applications like computer vision, natural language processing, and speech recognition.

The hardware that runs the latest AI systems faces rising uncertainty as models grow larger and more computationally intensive. The U.S. Commerce Department sounded an alarm over bottlenecks in the availability of semiconductor chips.

Google designed its own AI chip for its new smartphone — a snub to Qualcomm, the dominant chip vendor in Android phones. What’s new: Google debuted the Tensor chip last week

A new report projects a rosy future for the AI industry.What’s new: A study from market research firm IDC estimates that global revenues for AI software, hardware, and services will reach $341.8 billion in

Tesla unveiled its own AI chip and — surprise! — plans for a humanoid robot.What’s new: At Tesla’s AI Day promotional event, the company offered a first look at an upcoming self-driving computer powered by custom AI chips.

A neural network wrote the blueprint for upcoming computer chips that will accelerate deep learning itself. Google engineers used a reinforcement learning system to arrange the billions of minuscule transistors in an upcoming version of its Tensor Processing Unit (TPU) chips.

A major corporate acquisition could reshape the hardware that makes AI tick.What’s new: U.S. processor giant Nvidia, the world’s leading vendor of the graphics processing units (GPUs) that perform calculations for deep learning, struck a deal to purchase UK chip designer Arm for $40 billion.

Chips specially designed for AI are becoming much faster at training neural networks, judging from recent trials. MLPerf, an organization that’s developing standards for hardware performance in machine learning tasks, released results from its third benchmark competition.

A high-profile semiconductor startup made a bid for the future of AI computation. UK startup Graphcore released the Colossus Mk2, a processor intended to perform the matrix math calculations at the heart of deep learning more efficiently than other specialized processors.

The for-profit research organization OpenAI has a new supercomputer to help achieve its dream of building the world’s most sophisticated AI. Microsoft engineered the new hardware network to train immense models on thousands of images, texts, and videos simultaneously.