FPGA
2 stories filed under FPGA on Beyond Market Intelligence. The newest of them: “Generating 32x32 images from a microcontroller with 264KB of RAM” and “Anthropic builds a team to design custom chips for faster AI models”. Training an image diffusion model on a microcontroller with 264KB of RAM is a bold experiment, and the results are honest about the tradeoffs. Anthropic is assembling a team to design its own custom AI chips, a move that signals a deliberate shift toward co-designing hardware and models. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every FPGA story on Beyond Market Intelligence, newest first.
Generating 32x32 images from a microcontroller with 264KB of RAM
Training an image diffusion model on a microcontroller with 264KB of RAM is a bold experiment, and the results are honest about the tradeoffs. The Shrike lite's FPGA added parallel INT8 MAC engines, but the memory wall from I/O operations made it slower than the MCU alone, at 220 seconds per image versus 70. That contrast is a useful lesson in hardware bottlenecks. Many outputs were noisy, yet some images landed.
Anthropic builds a team to design custom chips for faster AI models
Anthropic is assembling a team to design its own custom AI chips, a move that signals a deliberate shift toward co-designing hardware and models. The Claude maker wants its technology to run faster and more efficiently, and that ambition starts at the silicon level. It's a smart bet on control and performance, even if the execution won't be simple. For anyone tracking how AI infrastructure evolves, this feels like a natural next step rather than a leap.