An Open Source Hardware-Software Blueprint for Flexible Deep Learning Specialization
Deep learning accelerators offer tremendous gains in compute throughput. That said, accelerators are only as good as the software compilers built to program them.
I will present VTA, a flexible deep learning accelerator design that is fully integrated and open-sourced with the Apache TVM deep learning compiler stack. I will describe the features and the abstraction layers that compose this complete hardware-software stack. Finally I will discuss how it has enabled researchers to perform full system evaluations of novel research ideas such as numerical system exploration.