VTA Deep Learning Accelerator

The Versatile Tensor Accelerator (VTA) is an extension of the TVM framework designed to advance deep learning and hardware innovation. VTA is a programmable accelerator that exposes a RISC-like programming abstraction to describe compute and memory operations at the tensor level. We designed VTA to expose the most salient and common characteristics of mainstream deep learning accelerators, such as tensor operations, DMA load/stores, and explicit compute/memory arbitration.


Checkout our blogpost, techreport VTA: An Open Hardware-Software Stack for Deep Learning, tutorials and guides and latest updates at tvm.ai.


Tianqi Chen
Assistant Professor - CMU
Thierry Moreau
Affiliate Assistant Professor
Luis Ceze
Carlos Guestrin
Professor - Stanford