Fully homomorphic encryption (FHE) enables the protection of data privacy at the cost of significantly higher computational demands. To alleviate its memory-bound bottlenecks, dataflow optimizations that maximize on-chip data reuse and minimize off-chip accesses could be leveraged. In this work, we exploit the opportunities of cross-operator dataflow optimizations in FHE accelerators, and propose a hardware-software co-design called CROPHE. On the hardware level, instead of overly-specialized functional units, CROPHE provisions a homogeneous and unified architecture that allows for flexible resource allocation and operator mapping. On the software level, the scheduling framework of CROPHE takes a comprehensive and systematic approach to explore various spatial and temporal data pipelining and sharing schemes across multiple operators, resulting in more efficient dataflow than prior work. We also propose novel cross-operator dataflow optimizations for the unique operators in FHE including number theoretic transforms and homomorphic rotations. The evaluation shows CROPHE significantly outperforms state-of-the-art designs by$1.77 \times$to$4.86 \times$.

With the ever-growing demands for online Artificial Intelligence (AI), the hardware virtualization support for deep learning accelerators is vital for providing AI capability in the cloud. Three basic features, multi-task, dynamic workload, and remote access, are fundamental for hardware virtualization. However, most of the deep learning accelerators do not support concurrent execution of multiple tasks. Besides, the SOTA multi-DNN scheduling algorithm for NN accelerators neither consider the multi-task concurrent execution and resources allocation for the multi-core DNN accelerators. Moreover, existing GPU virtualized solutions could introduce a huge remote access latency overhead, resulting in a severe system performance drop.