Name
Abstract | ||
|---|---|---|
Planet-scale applications are driving the exponential growth of the Cloud, and datacenter specialization is the key enabler of this trend. GPU- and FPGA-based clouds have already been deployed to accelerate compute-intensive workloads. ASIC-based clouds are a natural evolution as cloud services expand across the planet. ASIC Clouds are purpose-built datacenters comprised of large arrays of ASIC accelerators that optimize the total cost of ownership (TCO) of large, high-volume scale-out computations. On the surface, ASIC Clouds may seem improbable due to high NREs and ASIC inflexibility, but large-scale ASIC Clouds have already been deployed for the Bitcoin cryptocurrency system. This paper distills lessons from these Bitcoin ASIC Clouds and applies them to other large scale workloads such as YouTube-style video-transcoding and Deep Learning, showing superior TCO versus CPU and GPU. It derives Pareto-optimal ASIC Cloud servers based on accelerator properties, by jointly optimizing ASIC architecture, DRAM, motherboard, power delivery, cooling, and operating voltage. Finally, the authors examine the impact of ASIC NRE and when it makes sense to build an ASIC Cloud.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3399734 | Communications of the ACM |
DocType | Volume | Issue |
Journal | 63 | 7 |
ISSN | Citations | PageRank |
0001-0782 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Bedford Taylor | 1 | 1707 | 154.51 |
| Luis Vega | 2 | 18 | 2.52 |
| Moein Khazraee | 3 | 0 | 0.34 |
| Ikuo Magaki | 4 | 0 | 0.34 |
| Scott Davidson | 5 | 14 | 3.05 |
| Dustin Richmond | 6 | 24 | 4.77 |