Presentation
Hynify: A High-throughput and Unified Accelerator for Multi-Mode Nonparametric Statistics
DescriptionNonparametric statistics methods are a class of robust and potent statistics, which are widely used in various domains such as finance, medicine, and computer science. Such methods deliver an accurate estimation without an assumed data distribution. Moreover, they can handle discrete data with various data sources. Despite their desirable features, the calculation of large-scale nonparametric statistics is both compute- and memory-intensive, and the performance overhead hinders them from widespread usage.
This paper identifies that the key performance bottleneck lies in the rank-based operations which are intensively involved in variants of nonparametric statistics methods. These rank-based operations can thereby be fully accelerated and structurally reused among diverse statistics. We then introduce Hynify, a high-throughput and unified accelerator that facilitates a rich set of nonparametric statistics. To ensure comprehensiveness, we capture three primary computational paradigms of nonparametric statistical methods, namely, aggregation, pair-wise rank, and concordance, with the right architecture designs. To improve throughput, Hynify exploits fine-grained computation and pipelining for increased performance. We implement Hynify in FPGA demonstration and representative experimental results demonstrate that Hynify delivers up to 160x/21x throughput improvement over GPU and 64-core CPU, respectively, while achieving up to 781x/62x energy efficiency improvement.
This paper identifies that the key performance bottleneck lies in the rank-based operations which are intensively involved in variants of nonparametric statistics methods. These rank-based operations can thereby be fully accelerated and structurally reused among diverse statistics. We then introduce Hynify, a high-throughput and unified accelerator that facilitates a rich set of nonparametric statistics. To ensure comprehensiveness, we capture three primary computational paradigms of nonparametric statistical methods, namely, aggregation, pair-wise rank, and concordance, with the right architecture designs. To improve throughput, Hynify exploits fine-grained computation and pipelining for increased performance. We implement Hynify in FPGA demonstration and representative experimental results demonstrate that Hynify delivers up to 160x/21x throughput improvement over GPU and 64-core CPU, respectively, while achieving up to 781x/62x energy efficiency improvement.
Event Type
Research Manuscript
TimeTuesday, June 253:30pm - 3:45pm PDT
Location3010, 3rd Floor
Design
Emerging Models of Computation