Operated by the Power Information Technology Laboratory at the University of Tennessee, FNET/GridEye is a low-cost, quickly deployable GPS-synchronized wide-area frequency measurement network.

May 9, 2021 · At longer input lengths, our FNet model is significantly faster: when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).

FNET creates a safe and collaborative space for food companies across the supply chain to share and learn from each other in order to improve human rights for workers in their operations and …

At longer input lengths, our FNet model is significantly faster: when compared to the “efficient” Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).

Dec 11, 2023 · This article delves into FNet, a transformative architecture that reimagines the traditional transformer by discarding attention mechanisms entirely. Let's begin the journey to explore FNet, but first, let's look at the limitations of transformers.

FNET/GridEye is a low-cost, GPS-synchronized wide-area power system frequency measurement network. Highly accurate Frequency Disturbance Recorders (FDRs) developed at Virginia Tech are used to measure the frequency, phase angle, and voltage of the power signal found at ordinary 120-V electrical outlets.

FNET (Frequency monitoring Network; a.k.a. FNET/GridEye, GridEye) is a wide-area power system frequency measurement system. Using a type of phasor measurement unit (PMU) known as a frequency disturbance recorder (FDR), FNET/GridEye is able to measure the power system frequency, voltage, and angle very accurately.

We introduce a new model, FNet, that uses the Fourier Transform as a mixing mecha-nism. FNet offers an excellent compromise between speed, memory footprint, and accu-racy, achieving 92% and 97%, respectively, of the accuracy of BERT-Base and BERT-Large (Devlin et al., 2019) on the GLUE benchmark.

A PyTorch implementation of FNet from the paper FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, and Santiago Ontanon (arXiv).

The authors of the paper present FNet, a model that replaces self-attention with standard unparametrized Fourier transforms. Not only is FNet faster and computationaly more efficient than the classic transformer, but it also retains 92% of BERT's accuracy on the GLUE benchmark.