  The flourish of deep learning frameworks and hardware platforms has been
demanding an efficient compiler that can shield the diversity in both software
and hardware in order to provide application portability. Among the existing
deep learning compilers, TVM is well known for its efficiency in code
generation and optimization across diverse hardware devices. In the meanwhile,
the Sunway many-core processor renders itself as a competitive candidate for
its attractive computational power in both scientific computing and deep
learning workloads. This paper combines the trends in these two directions.
Specifically, we propose swTVM that extends the original TVM to support
ahead-of-time compilation for architecture requiring cross-compilation such as
Sunway. In addition, we leverage the architecture features during the
compilation such as core group for massive parallelism, DMA for high bandwidth
memory transfer and local device memory for data locality, in order to generate
efficient codes for deep learning workloads on Sunway. The experiment results
show that the codes generated by swTVM achieves 1.79x on average compared to
the state-of-the-art deep learning framework on Sunway, across six
representative benchmarks. This work is the first attempt from the compiler
perspective to bridge the gap of deep learning and Sunway processor
particularly with productivity and efficiency in mind. We believe this work
will encourage more people to embrace the power of deep learning and Sunway
many-core processor.
