Deep Learning Programming at Scale
Deep learning has become one of the most important computational workloads of our generation, advancing applications across industries from healthcare to autonomous driving. But it is also profoundly computationally intensive.
To train today’s state-of-the-art neural networks, researchers often have to use large clusters of dozens to hundreds of graphics processing units (GPUs). These clusters are expensive to build, complicated to program for, and can still take days to weeks to train a network, dragging the pace of innovation. We founded Cerebras to solve this problem.