Liquid Silicon-Monona: A Reconfigurable Memory-Oriented Computing Fabric with Scalable Multi-Context Support

Abstract

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 competitive advantages in performance and energy efficiency for diverse cloud workloads: 1) limited configuration capability for supporting light-weight computations/on-chip data storage to accelerate emerging search-/data-intensive applications. 2) lack of architectural support to hide reconfiguration overhead for assisting virtualization in a cloud computing environment. In this paper, we propose a reconfigurable memory-oriented computing fabric, namely Liquid Silicon-Monona (L-Si), enabled by emerging nonvolatile memory technology i.e. RRAM, to address these two challenges. Specifically, L-Si addresses the first challenge by virtue of a new architecture comprising a 2D array of physically identical but functionally-configurable building blocks. It, for the first time, extends the configuration capabilities of existing FPGAs from computation to the whole spectrum ranging from computation to data storage. It allows users to better customize hardware by flexibly partitioning hardware resources between computation and memory, greatly benefiting emerging search- and data-intensive applications. To address the second challenge, L-Si provides scalable multi-context architectural support to minimize reconfiguration overhead for assisting virtualization. In addition, we provide compiler support to facilitate the programming of applications written in high-level programming languages (e.g. OpenCL) and frameworks (e.g. TensorFlow, MapReduce) while fully exploiting the unique architectural capability of L-Si. Our evaluation results show L-Si achieves 99.6% area reduction, 1.43× throughput improvement and 94.0% power reduction on search-intensive benchmarks, as compared with the FPGA baseline. For neural network benchmarks, on average, L-Si achieves 52.3× speedup, 113.9× energy reduction and 81% area reduction over the FPGA baseline. In addition, the multi-context architecture of L-Si reduces the context switching time to - 10ns, compared with an off-the-shelf FPGA (∼100ms), greatly facilitating virtualization.