Cloud providers have widely adopted FPGAs to meet the high-performance, energy-efficient demands of cloud workloads. While they offer homogeneous FPGAs per service, recent FPGA products exhibit increasing heterogeneity in terms of vendors, capacity, off-chip memory, and performance. These diverse properties not only render applications incompatible across different FPGAs but also lead to performance disparities and resource inefficiency.

The adoption of FPGAs in cloud-native environments is facing impediments due to FPGA limitations and CPU-oriented design of orchestrators, as they lack virtualization, isolation, and preemption support for FPGAs. Consequently, cloud providers offer no orchestration services for FPGAs, leading to low scalability, flexibility, and resiliency.

FPGAs provide a programmable, energy-efficient, and compute-intensive acceleration substrate; thus, on the one hand, they offer a compelling solution for optimizing serverless workloads in cloud environments. On the other hand, FPGAs also introduce significant challenges that directly contradict the serverless model in the cloud, including their low-level and complex programming APIs, lack of virtualization and isolation mechanisms, high reconfiguration and communication overheads, and absence of orchestration mechanisms.

Unikernels specialize operating systems by tailoring the kernel for a specific application at compile time. While the specialized library OS approach provides a smaller OS image-thus improving the bootup process, performance, migration costs, and reliable/trusted computing base---at the same time, unikernels lack run-time extensibility, which is imperative to support "on-demand" auxiliary tasks and tools, e.g., debugging, monitoring, re-configuration, and system management and deployment in a typical cloud environment. Consequently, unikernels present a fundamental trade-off between slimness of the OS image size at the compile time vs. flexibility of supported auxiliary functionality at the run-time.

The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

We present an end-to-end solution to facilitate the seamless execution of hardware-accelerated compute-intensive tasks on heterogeneous hardware platforms spanning the Cloud-Edge continuum. Our approach includes a programming interface, orchestration, application management components, the vAccel framework, and a library of hardware-accelerated kernels. These components enable a Function-as-a-Service (FaaS) based operational flow that supports numerous diverse use cases while minimizing the time required for the developer to integrate their code and for the vendor to provide hardware acceleration capabilities to end users. Experimental results showcase the merits of our approach.