Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs. (arXiv:2204.14007v1 [cs.DC])

On-device ML accelerators are becoming a standard in modern mobile
system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for
efficiently utilizing the high compute throughput offered by these
accelerators. However, existing NAS frameworks have several practical
limitations in scaling to multiple tasks and different target platforms. In
this work, we provide a two-pronged approach to this challenge: (i) a
NAS-enabling infrastructure that decouples model cost evaluation, search space
design, and the NAS algorithm to rapidly target various on-device …

arxiv edge ml neural architectures tpus