Title: VTA: An Open Hardware-Software Stack for Deep Learning,
Abstract: Hardware acceleration is an enabler for ubiquitous and efficient deep
learning. With hardware accelerators being introduced in datacenter and edge
devices, it is time to acknowledge that hardware specialization is central to
the deep learning system stack.
This technical report presents the Versatile Tensor Accelerator (VTA), an
open, generic, and customizable deep learning accelerator design. VTA is a
programmable accelerator that exposes a RISC-like programming abstraction to
describe 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.
VTA is more than a standalone accelerator design: it's an end-to-end solution
that includes drivers, a JIT runtime, and an optimizing compiler stack based on
TVM. The current release of VTA includes a behavioral hardware simulator, as
well as the infrastructure to deploy VTA on low-cost FPGA development boards
for fast prototyping.
By extending the TVM stack with a customizable, and open source deep learning
hardware accelerator design, we are exposing a transparent end-to-end deep
learning stack from the high-level deep learning framework, down to the actual
hardware design and implementation. This forms a truly end-to-end, from
software-to-hardware open source stack for deep learning systems.