ai hardware

Infrastructure2 hours ago

Building Fixed Hardware Implementations of Neural Networks

Researchers from Yale, Cornell, Boston University, and NTT Research published "Physical Foundation Models: Fixed hardware implementations of large-scale neural networks," arguing that the ~1-year release cadence of trillion-parameter foundation models justifies building fixed, special-purpose hardware where the network is realized directly in physical structure (such as 3D nanostructured glass) rather than executed on general-purpose digital inference chips.

Why it matters

The economic argument here matters more than the optics demo. The authors are saying that once a single foundation model gets reused across thousands of downstream tasks, the amortization math flips: it becomes rational to tape out hardware whose weights are physically baked in, on the same yearly cadence as model releases. That is a direct challenge to the Nvidia general-purpose GPU thesis for inference, and it is the same logic Groq, Etched (Sohu), and MatX are already pursuing in digital silicon. PFMs push it one step further into analog physics.

For operators, the near-term read is not glass holograms running GPT-class models. It is edge inference: trillion-parameter capability in a power envelope a robot or drone can carry, which today caps out at far smaller models. If even a fraction of the claimed energy-efficiency gain materializes in nanoelectronic form, the on-device vs cloud calculus for humanoids and AVs changes. Watch NTT Research specifically; they have been quietly funding coherent optical compute for years and are the most likely party to move this from arXiv to a fab partnership.

BonusMay 23

The future of physical AI isn’t humanoid; it’s task-specific and cost-efficient

Source: The Robot Report

Hailo's vice president of physical AI, Yaniv Sulkes, argues in a Robot Report contributed piece that task-specific robots running edge AI will scale faster than general-purpose humanoids, citing hardware, dexterity, energy, and cost constraints. He points to deployments like Husqvarna's AI-enabled robotic lawn mowers, which use Hailo edge processors for on-device sense-think-act loops, as the template.

Why it matters

This is a Hailo positioning piece, and the cap-table read matters: Hailo sells edge inference chips and loses if the humanoid narrative consolidates compute onto Nvidia Jetson-class modules or cloud-tethered VLAs. The argument is still directionally right for operators sizing 2026 deployments. The unit volumes already shipping (robotic vacuums, lawn mowers like Husqvarna's, last-mile delivery, ag sprayers) all clear the bar Hailo describes: bounded task, embedded inference, no cloud in the control loop. Humanoid pilots are not in that category yet.

The number to watch is bill-of-materials. A task-specific robot built around a sub-$50 edge accelerator and a fixed-function actuator stack pencils out at consumer or light-commercial price points. A humanoid with 40-plus degrees of freedom and a 100W-class compute module does not, regardless of how good the foundation model gets. Operators evaluating physical AI vendors should ask where inference runs, what the per-unit silicon cost is, and whether the control loop survives a dead network. Those three questions separate shipping product from demo reels.

LaunchesMay 22

We tried Google’s AI glasses and they’re almost there

Source: AI News & Artificial Intelligence | TechCrunch

At Google I/O, Google demoed prototype Android XR glasses with an in-lens display running Gemini for translation, turn-by-turn navigation via Google Maps, photo capture, object recognition, and configurable widgets. \

DealsMay 21

Hark Raises $700M at $6B Valuation for Figure Founder's AI Hardware Venture

Source: TechCrunch

Brett Adcock's AI hardware startup Hark raised over $700 million in a Series A round valuing the company at $6 billion. Hark is building a vertically integrated family of AI hardware devices with co-developed models and interfaces; notably, Hark's models are already being trained on Adcock's Figure AI robots, creating a cross-company embodied-AI data loop.