A general-purpose in-memory computing architecture that addresses several key fundamental limitations of state-of-the-art reconfigurable data-flow architectures in supporting emerging machine learning and big data applications
With the recent trend of promoting Field-Programmable Gate Arrays (FPGAs) to first-class citizens in accelerating compute-intensive applications in networking, cloud services and artificial intelligence, FPGAs face two major challenges in sustaining …
This paper presents a data-centric reconfigurable architecture, namely Liquid Silicon, enabled by emerging non-volatile memory, i.e., RRAM. Compared to the heterogeneous architecture of commercial FPGAs, Liquid Silicon is inherently a homogeneous …