Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack

Autor:	Diamantopoulos, Dionysios, Ringlein, Burkhard, Purandare, Mitra, Singh, Gagandeep, Hagleitner, Christoph
Rok vydání:	2020
Předmět:	Computer Science - Distributed Parallel and Cluster Computing Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
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
Externí odkaz:	http://arxiv.org/abs/2004.10854 Zobrazit plný text záznamu View this record from Arxiv