Elements: Portable Library for Homomorphic Encrypted Machine Learning on FPGA Accelerated Cloud Cyberinfrastructure

利用同态加密直接对加密数据进行计算的隐私保护计算（PPC）近来变得很有吸引力。基于HE的机器学习（HE ML）推理能够在医疗保健、金融交易、边缘网络物理系统等各种应用领域中保护隐私。依赖公共云或数据中心处理的隐私敏感应用可以使用HE ML推理来保护隐私。虽然HE ML推理提供了强大的隐私保证，但加密数据的计算比未加密的计算慢几个数量级，并且需要大量的硬件资源才能对最终用户有吸引力。新兴的数据中心和云平台通过现场可编程门阵列（FPGA）得到增强。凭借FPGA的细粒度可编程架构，这些平台非常适合加速HE ML。这项工作将利用FPGA上的新颖算法，架构和内存优化来开发一个便携式和可配置的库，以实现安全，弹性和值得信赖的网络基础设施，用于端到端隐私敏感的ML推理。该库将为HE内核（L1库）提供FPGA加速的知识产权（IP）内核，以及FPGA专用处理器（ASP），用于推断广泛研究的HE ML模型（L2库）。该库将支持各种HE方案，安全级别，机器学习模型和FPGA平台。它将包括几个软件和硬件创新沿着各种HE特定的优化，如高效的数据布局，内存高效调度和可扩展的互连，以最大限度地提高内存利用率，并提高数据重用使用片上存储器。利用L1库中的IP核，本项目将组成一个具有特定领域指令集体系结构（伊萨）和编译器的FPGA ASP。FPGA加速器可以在软件中编程以实现实时HE ML计算。该库将发布给计算机信息科学工程（CISE）社区，包括机器学习、软件和数据科学社区，以加速同态加密在隐私保护计算中的采用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Privacy Preserving Computations (PPC) that utilize Homomorphic Encryption (HE) to perform computations on encrypted data directly have become attractive recently. HE based Machine Learning (HE ML) inference enables preservation of privacy in a wide variety of application domains that range from healthcare, financial transactions, edge cyber physical systems, etc. Privacy sensitive applications that rely on processing in a public cloud or data center can use HE ML inference to preserve privacy. While HE ML inference offers strong privacy guarantees, computations on encrypted data are orders of magnitude slower than unencrypted computations and require significant hardware resources to make them attractive for end users. Emerging data centers and cloud platforms are augmented with Field Programmable Gate Arrays (FPGAs). With the fine grained programmable architecture of FPGAs, these platforms are well suited for accelerating HE ML.This work will leverage novel algorithmic, architectural and memory optimizations on FPGAs to develop a portable and configurable library to enable secure, resilient, and trustworthy cyberinfrastructure for end-to-end privacy sensitive ML inference. The library will provide FPGA accelerated Intellectual Property (IP) cores for HE kernels (L1 Library) as well as a FPGA Application Specific Processor (ASP) for inference of widely studied HE ML models (L2 Library). The library will support various HE schemes, security levels, machine learning models and FPGA platforms. It will include several software and hardware innovations along with various HE specific optimizations such as efficient data layout, memory efficient scheduling and scalable interconnect to maximize memory utilization and to improve data reuse using on-chip memory. Using the IP cores in the L1 Library, this project will compose a FPGA ASP with a domain-specific Instruction Set Architecture (ISA) and a compiler. The FPGA accelerator can be programmed in software to realize real-time HE ML computations. The library will be released to the Computer & Information Science & Engineering (CISE) communities, including Machine Learning, Software, and Data Science communities, to accelerate the adoption of homomorphic encryption for privacy preserving computations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.