Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack
In: 2020 30th International Conference on Field-Programmable Logic and Applications (FPL) FPL International Conference on Field Programmable Logic and Applications
Online
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Zugriff:
Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have been emerged as powerful architecture choices for high-performance Deep-Learning computing. The rapid development of frameworks, models, and precision options challenges the adaptability of such tensor-accelerators since the adaptation to new requirements incurs significant engineering costs. Programmable tensor accelerators offer a promising alternative by allowing reconfiguration of a virtual architecture that overlays on top of the physical FPGA configurable fabric. We propose an overlay ({\tau}-VTA) and an optimization method guided by agile-inspired auto-tuning techniques. We achieve higher performance and faster convergence than state-of-art.
Comment: 9 pages, 7 figures
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Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack
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Autor/in / Beteiligte Person: | Singh, Gagandeep ; Ringlein, Burkhard ; Diamantopoulos, Dionysios ; Purandare, Mitra ; Hagleitner, Christoph |
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Quelle: | 2020 30th International Conference on Field-Programmable Logic and Applications (FPL) FPL International Conference on Field Programmable Logic and Applications |
Veröffentlichung: | IEEE |
Medientyp: | unknown |
ISBN: | 978-1-7281-9902-3 (print) |
DOI: | 10.1109/fpl50879.2020.00058 |
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