Name
Title | ||
|---|---|---|
Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack |
Abstract | ||
|---|---|---|
Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have 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 (τ-VTA) and an optimization method guided by agile-inspired auto-tuning techniques. We achieve higher performance of up to 2.5x and faster convergence of up to 8.1x. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/FPL50879.2020.00058 | 2020 30th International Conference on Field-Programmable Logic and Applications (FPL) |
Keywords | DocType | ISBN |
Neural Networks, Machine Learning, Autotuning, FPGA, Transprecision Computing, Tensor Accelerator | Conference | 978-1-7281-9902-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dionysios Diamantopoulos | 1 | 26 | 7.11 |
| Ringlein Burkhard | 2 | 0 | 0.34 |
| Mitra Purandare | 3 | 81 | 5.49 |
| Gagandeep Singh | 4 | 78 | 11.52 |
| Christoph Hagleitner | 5 | 108 | 20.84 |