Hardware & compute Papers

A 1982 paper by John J. Hopfield arguing that useful computation can emerge as a collective property of many simple, equivalent units, and describing a model whose collective behaviour gives content-addressable memory, generalisation and error correction.

A 2009 ICML paper by Rajat Raina, Anand Madhavan and Andrew Y. Ng that used graphics processors to train deep belief networks and sparse coding models many times faster than multicore CPUs.

The 2012 NeurIPS paper that introduced AlexNet, a large convolutional network trained on GPUs. It won the ILSVRC-2012 image classification competition by a wide margin and is widely seen as a turning point for deep learning in computer vision.

An NVIDIA paper from 2019 presenting an intra-layer model parallelism method, implemented with a few communication operations in native PyTorch, that trained transformer models of up to 8.3 billion parameters and improved several benchmarks.

A 2020 paper finding that language model loss falls predictably, as a power law, with model size, data and compute. It gave labs a quantitative reason to build much larger models and shaped the scale-up that followed.

A 2012 NIPS paper by Jeffrey Dean, Andrew Ng and colleagues that introduced DistBelief, a framework for training deep networks across clusters of thousands of machines, together with the Downpour SGD and Sandblaster optimisation procedures.

A 2022 Nature paper by Degrave, Felici and colleagues showing that a deep reinforcement learning controller can learn on its own to command all of a tokamak's magnetic control coils, producing and holding a range of plasma shapes.