Cancer Surgeons, Photo by — @nci

Memory Usage in Medical AI Applications

Addressing the memory bottleneck in AI model training

With increasing application of medical imaging, and advanced analysis with machine learning techniques there is a need to increase the efficiency of these applications. One way to do so is the combination of software and hardware. Part of the research team at Intel proposes they have some solutions.

An article published by Tony Reina, Prashant Shah, David Ojika, Bhavesh Patel and Trent Boyer attempts to do so. It is called Addressing the Memory Bottleneck in AI Model Training.

Of course this is in a way a form of a product pitch from researchers working at Intel to buy an Intel product.

Although this is the case it does not hurt to look at their proposition.

They argue healthcare workloads can be processed more efficient.

Intel AI research shared this on their Twitter account.

Max Planck Institute worked with Intel to run inference on a full 3D dataset for a 3D brain imaging model.

One of their achievements was to: “…reduce the original 24 TB memory requirement by a factor of 16 via efficient reuse of memory enabled by the Intel® Distribution of OpenVINO™ toolkit.”

Doing so processing each image required only 1.5 TB of RAM to perform AI inference, and processing took less than an hour compared to 24 hours during initial tests.

This can be represented as the following.

I do not have any answer, rather than an interesting direction.

Since medical imaging needs to become more efficient and require less energy this seems an important area to explore.

If we are to implement solutions within the field of artificial intelligence on a large scale there are likely many who will work on this both to offer better products, but hopefully also to reduce the footprint of the given AI solutions.

This is #500daysofAI and you are reading article 314. I am writing one new article about or related to artificial intelligence every day for 500 days. My focus for day 300–400 is about AI, hardware and the climate crisis.

AI Policy and Ethics at Student at University of Copenhagen MSc in Social Data Science. All views are my own.