A recent paper published on arXiv by a team of UC Berkeley researchers observes that neural networks are increasingly bottlenecked and constrained by the limited capacity of on-device GPU memory. Indeed, deep learning is constantly testing the limits of memory capacity on neural network accelerators as neural networks train with high-resolution images, 3D point-clouds and long Natural Language Processing (NLP) sequence data.
“In these applications, GPU memory usage is dominated by the intermediate activation tensors needed for backpropagation. The limited availability of high bandwidth on-device memory creates a memory wall that stifles exploration of novel architectures,” the researchers explain. “One of the main challenges when training large neural networks is the limited capacity of high-bandwidth memory on accelerators such as GPUs and TPUs. Critically, the bottleneck for state-of-the-art model development is now memory rather than data and compute availability and we expect this trend to worsen in the near future.”
As the researchers point out, some initiatives to address this bottleneck focus on dropping activations as a strategy to scale to larger neural networks under memory constraints. However, these heuristics assume uniform per-layer costs and are limited to simple architectures with linear graphs. As such, the UC Berkeley team uses off-the-shelf numerical solvers to formulate optimal rematerialization strategies for arbitrary deep neural networks in TensorFlow with non-uniform computation and memory costs. In addition, the UC Berkeley team demonstrates how optimal rematerialization enables larger batch sizes and substantially reduced memory usage – with minimal computational overhead across a range of image classification and semantic segmentation architectures.
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