Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack
Autor: | Diamantopoulos, Dionysios, Ringlein, Burkhard, Purandare, Mitra, Singh, Gagandeep, Hagleitner, Christoph |
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Rok vydání: | 2020 |
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Druh dokumentu: | Working Paper |
Popis: | 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 |
Databáze: | arXiv |
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