> can potentially reduce 95% energy cost by elementwise floating point tensor multiplications and 80% energy cost of dot products
It this were about convolutional nets then optimizing compute would be a much bigger deal. Transformers are lightweight on compute and heavy on memory. The weakest link in the chain is fetching the model weights into the cores. The 95% and 80% energy reductions cited are for the multiplication operations in isolation, not for the entire inference process.
It this were about convolutional nets then optimizing compute would be a much bigger deal. Transformers are lightweight on compute and heavy on memory. The weakest link in the chain is fetching the model weights into the cores. The 95% and 80% energy reductions cited are for the multiplication operations in isolation, not for the entire inference process.