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Found 4 papers, 1 papers with code
Confidential Machine Learning within Graphcore IPUs
We present IPU Trusted Extensions (ITX), a set of experimental hardware extensions that enable trusted execution environments in Graphcore's AI accelerators.
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities.
Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently even for small minibatch sizes, resulting in significantly shorter overall training times.
