Cost-effective Hardware Accelerator Recommendation for Edge Computing

Hardware accelerator devices have emerged as an alternative to traditional CPUs since they not only help perform computations faster but also consume much lessenergy than a traditional CPU thereby helping to lower both capex (i.e., procurement costs) and opex (i.e., energy usage). However, since different accelerator tech-nologies can illustrate different traits for different application types that run at the edge, there is a critical needfor effective mechanisms that can help developers select the right technology (or a mix of) to use in their context,which is currently lacking. To address this critical need,we propose a recommender system to help users rapidly and cost-effectively select the right hardware accelerator technology for a given compute-intensive task. Our framework comprises the following workflow. First, we collect realistic execution traces of computations on real,single hardware accelerator devices. Second, we utilize these traces to deduce the achievable latencies and amortized costs for device deployments across the cloud-edge spectrum, which in turn provides guidance in selecting the right hardware.