Abstract
Cloud services are becoming increasingly globalized and data-center workloads are expanding exponentially. GPU and FPGA-based clouds have illustrated improvements in power and performance by accelerating compute-intensive workloads. ASIC-based clouds are a promising way to optimize the Total Cost of Ownership (TCO) of a given datacenter computation (e.g. YouTube transcoding) by reducing both energy consumption and marginal computation cost.The feasibility of an ASIC Cloud for a particular application is directly gated by the ability to manage the Non-Recurring Engineering (NRE) costs of designing and fabricating the ASIC, so that it is significantly lower (e.g. 2X) than the TCO of the best available alternative.In this paper, we show that technology node selection is a major tool for managing ASIC Cloud NRE, and allows the designer to trade off an accelerator's excess energy efficiency and cost performance for lower total cost.We explore NRE and cross-technology optimization of ASIC Clouds for four different applications: Bitcoin mining, YouTube-style video transcoding, Litecoin, and Deep Learning. We address these challenges and show large reductions in the NRE, potentially enabling ASIC Clouds to address a wider variety of datacenter workloads. Our results suggest that advanced nodes like 16nm will lead to sub-optimal TCO for many workloads, and that use of older nodes like 65nm can enable a greater diversity of ASIC Clouds.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.