EfficientBioAI: Making Bioimaging AI Models Efficient in Energy, Latency
  and Representation
Yu Zhou Justin Sonneck Sweta Banerjee Stefanie Dörr Anika Grüneboom Kristina Lorenz Jianxu Chen
Artificial intelligence (AI) has been widely used in bioimage image analysis
nowadays, but the efficiency of AI models, like the energy consumption and
latency is not ignorable due to the growing model size and complexity, as well
as the fast-growing analysis needs in modern biomedical studies. Like we can
compress large images for efficient storage and sharing, we can also compress
the AI models for efficient applications and deployment. In this work, we
present EfficientBioAI, a plug-and-play toolbox that can compress given
bioimaging AI models for them to run with significantly reduced energy cost and
inference time on both CPU and GPU, without compromise on accuracy. In some
cases, the prediction accuracy could even increase after compression, since the
compression procedure could remove redundant information in the model
representation and therefore reduce over-fitting. From four different bioimage
analysis applications, we observed around 2-5 times speed-up during inference
and 30-80$\%$ saving in energy. Cutting the runtime of large scale bioimage
analysis from days to hours or getting a two-minutes bioimaging AI model
inference done in near real-time will open new doors for method development and
biomedical discoveries. We hope our toolbox will facilitate
resource-constrained bioimaging AI and accelerate large-scale AI-based
quantitative biological studies in an eco-friendly way, as well as stimulate
further research on the efficiency of bioimaging AI.