DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks
Sweta Priyadarshi Tianyu Jiang Hsin-Pai Cheng Sendil Krishna Viswanath Ganapathy Chirag Patel
With the growing demand for vision applications and deployment across edge
devices, the development of hardware-friendly architectures that maintain
performance during device deployment becomes crucial. Neural architecture
search (NAS) techniques explore various approaches to discover efficient
architectures for diverse learning tasks in a computationally efficient manner.
In this paper, we present the next-generation neural architecture design for
computationally efficient neural architecture distillation - DONNAv2 .
Conventional NAS algorithms rely on a computationally extensive stage where an
accuracy predictor is learned to estimate model performance within search
space. This building of accuracy predictors helps them predict the performance
of models that are not being finetuned. Here, we have developed an elegant
approach to eliminate building the accuracy predictor and extend DONNA to a
computationally efficient setting. The loss metric of individual blocks forming
the network serves as the surrogate performance measure for the sampled models
in the NAS search stage. To validate the performance of DONNAv2 we have
performed extensive experiments involving a range of diverse vision tasks
including classification, object detection, image denoising, super-resolution,
and panoptic perception network (YOLOP). The hardware-in-the-loop experiments
were carried out using the Samsung Galaxy S10 mobile platform. Notably, DONNAv2
reduces the computational cost of DONNA by 10x for the larger datasets.
Furthermore, to improve the quality of NAS search space, DONNAv2 leverages a
block knowledge distillation filter to remove blocks with high inference costs.