Picasso: An Area/Energy-Efficient End-to-End Diffusion Accelerator with Hyper-Precision Data Type

This work presents Picasso, an end-to-end diffusion accelerator designed for enhancing the efficiency of diffusion-based machine learning models used in applications such as image and video generation, and inpainting. Picasso introduces a novel hyper-precision 8 (HYP8) data type and a reconfigurable architecture designed to significantly enhance hardware efficiency, providing an extended dynamic range without sacrificing accuracy. It also features a unified engine that streamlines the processing of all non-matrix operations and employs sub-block pipeline scheduling to reduce overall latency. Fabricated in 28nm CMOS technology, this accelerator achieves an energy efficiency of 4.96 TOPS/W and a peak performance of 9.83 TOPS. Compared to previous works, Picasso demonstrates speedups ranging from 8.4× to 26.8× while also improving energy and area efficiency by 1.1× to 2.8× and 3.6× to 30.5×, respectively.