Towards Efficient HW Acceleration in Edge-Cloud Infrastructures: The SERRANO Approach - Invited Paper.

Nowadays, we witness an ever-increased number of applications deployed over Edge, Cloud and HPC infrastructures. This rapid explosion of computing devices across the computing continuum poses new challenges in terms of providing a power-efficient, secure and automatic way for deployment of different applications in such heterogeneous environments. Moreover, the need for performance efficient deployments within such environments, has introduced the presence of hardware accelerators over the entire computing stack. In this paper, we present SERRANO's approach for providing efficient HW accelerated deployments over edge-cloud infrastructures. First, we give a brief overview of the SERRANO project, describing its goals and objectives, providing a high-level overview of SERRANO's platform architecture and presenting the use-cases involved. Then, we describe SERRANO's approach for providing efficient HW accelerators by identifying trade-offs between performance, accuracy and power consumption and also demonstrate how SERRANO aims to automate the optimization process through machine learning models in order to construct a generic optimization heuristic to fine-tune programs for both GPU and FPGA accelerators. Through some illustrative examples, we showcase that by applying approximation and optimization techniques, we are able to achieve an average decrease of 28% in power consumption for FPGA devices and trade-off between performance and power usage for GPUs, achieving up to x1.21 speedups and 8% power improvement.