Archive of posts tagged: Accelerators
The Future of Sparsity in Deep Neural Networks
Sparsity in Deep Neural Networks The key characteristic of deep learning is that accuracy empirically scales with the size of the model and the amount of training data. Over the past decade, this property has enabled dramatic improvements in the state of the art...
Chiplet-Based Systems
[Editor’s Note: I’m very happy to announce that Christina Delimitrou of Cornell University will be serving as the blog’s Associate Editor. Thank you, Vijay Janapa Reddi, for your amazing service in this role for the last three years!] Chip vendors...
From Heavy Metal to Irrational Exuberance
The focus of most published research in architecture is on applications implemented in high-performance, “close-to-the-metal” languages essentially developed before computers got fast. These, let’s call them metal languages, include FORTRAN...
A Case for Optical Deep Neural Networks
Deep Neural Networks have been a major focus for computer architects in the recent past due to the massive parallelism available in computation, combined with the massive amount of data re-use. While the proposed architectures have inspired industry innovations such...Subscribe
