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Google's Cloud TPUs are a promising new hardware architecture for machinelearning workloads. They have powered many of Google's milestone machinelearning achievements in recent years. Google has now made TPUs available forgeneral use on their cloud platform and as of very recently has opened them upfurther to allow use by non-TensorFlow frontends. We describe a method andimplementation for offloading suitable sections of Julia programs to TPUs viathis new API and the Google XLA compiler. Our method is able to completely fusethe forward pass of a VGG19 model expressed as a Julia program into a singleTPU executable to be offloaded to the device. Our method composes well withexisting compiler-based automatic differentiation techniques on Julia code, andwe are thus able to also automatically obtain the VGG19 backwards pass andsimilarly offload it to the TPU. Targeting TPUs using our compiler, we are ableto evaluate the VGG19 forward pass on a batch of 100 images in 0.23s whichcompares favorably to the 52.4s required for the original model on the CPU. Ourimplementation is less than 1000 lines of Julia, with no TPU specific changesmade to the core Julia compiler or any other Julia packages.

Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs