Using Diffusion Models to Generate Chip Placements (UC Berkeley)


A technical paper titled “Chip Placement with Diffusion” was published by researchers at UC Berkeley. Abstract: "Macro placement is a vital step in digital circuit design that defines the physical location of large collections of components, known as macros, on a 2-dimensional chip. The physical layout obtained during placement determines key performance metrics of the chip, such as power... » read more

Considerations For Accelerating On-Device Stable Diffusion Models


One of the more powerful – and visually stunning – advances in generative AI has been the development of Stable Diffusion models. These models are used for image generation, image denoising, inpainting (reconstructing missing regions in an image), outpainting (generating new pixels that seamlessly extend an image's existing bounds), and bit diffusion. Stable Diffusion uses a type of dif... » read more