ISA mapper: a compute and hardware agnostic deep learning compiler
Domain specific accelerators present new challenges for code generation onto novel instruction sets, communication fabrics, and memory architectures. We introduce a shared intermediate representation to describe both deep learning programs and hardware capabilities, then formulate and apply instruction mapping to determine how a computation can be performed on a hardware system. Our scheduler chooses a specific mapping and determines data movement and computation order. With this system, we demonstrate automated extraction of matrix multiplication kernels from recent deep learning operations. We demonstrate 2 5X better performance on GEMM and GRU execution versus state of the art on new hardware and up to 85% of state of the art performance on existing hardware.
CF