2022 26th International Conference on Engineering of Complex Computer Systems (ICECCS)
Software Architecture -- Applications
Deep-learning accelerators are increasingly popular. There are two prevalent accelerator architectures: one based on general matrix multiplication units and the other on convolution cores. However, Tensor Virtual Machine (TVM), a widely used deep-learning compiler stack, does not support the latter. This paper proposes a general framework for extending TVM to support deep-learning accelerators with convolution cores. We have applied it to two well-known accelerators: Nvidia's NVDLA and Bitmain's BM1880 successfully. Deep-learning workloads can now be readily deployed to these accelerators through TVM and executed efficiently. This framework can extend TVM to other accelerators with minimum effort.
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Published as: Wang, Y., & Xie, F. (2022, March). Extending Tensor Virtual Machine to Support Deep-Learning Accelerators with Convolution Cores. In 2022 26th International Conference on Engineering of Complex Computer Systems (ICECCS) (pp. 189-194). IEEE.