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IPU programming in Julia

Python implementation of Iterative Proportional Updating (IPU). The IPU algorithm is a general case of iterative proportional fitting that can satisfy two disparate sets of marginals that do not agree on a single total.

An implementation of the Search by Triplet track reconstruction algorithm on the Graphcore IPU.

A PyTorch library for Knowledge Graph Embedding on Graphcore IPUs implementing the distribution framework BESS

TessellateIPU: low level Poplar tile programming from Python

Poplar implementation of FlashAttention for IPU

PySCF on IPU

JAX for Graphcore IPU (experimental)

Poplar Advanced Runtime for the IPU

Code for CoNLL BabyLM workshop Mini Minds: Exploring Bebeshka and Zlata Baby Models

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TensorFlow for the IPU

PyTorch interface for the IPU

We evaluate SNNs' performance and energy efficiency on CPUs, GPUs, and IPUs using a simulation framework and benchmark dataset. Our study aims to identify the optimal computing device for SNNs and contribute to energy-efficient machine learning.

Code associated with my master thesis at the University of Bergen

IPU-specific extensions to PopTorch

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Track reconstruction on the Graphcore IPU.