  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.
