Name
Abstract | ||
|---|---|---|
Given the growth in data inputs and application complexity, it is often the case that a single hardware accelerator is not enough to solve a given problem. In particular, the computational demands and I/O of many tasks in machine learning often require a cluster of accelerators to make a relevant difference in performance. In this article, we explore the efficient construction of FPGA clusters using inference over Decision Tree Ensembles as the target application. The article explores several levels of the problem: (1) a lightweight inter-FPGA communication protocol and routing layer to facilitate the communication between the different FPGAs, (2) the data partitioning and distribution strategies maximizing performance, (3) and an in depth analysis on how applications can be efficiently distributed over such a cluster. The experimental analysis shows that the resulting system can support inference over decision tree ensembles at a significantly higher throughput than that achieved by existing systems.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3340263 | ACM Transactions on Reconfigurable Technology and Systems (TRETS) |
Keywords | Field | DocType |
Decision trees, FPGA cluster, Intel HARP, Microsoft Catapult, distributed systems, inference, machine learning | Cluster (physics),Decision tree,Inference,Computer science,Parallel computing,Field-programmable gate array,Hardware acceleration,Throughput,Data partitioning,Distributed computing,Communications protocol | Journal |
Volume | Issue | ISSN |
12 | 4 | 1936-7406 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhsen Owaida | 1 | 86 | 9.65 |
| Amit Kulkarni | 2 | 1 | 0.36 |
| Gustavo Alonso | 3 | 5476 | 612.79 |