Search Results for author:
Found 5 papers, 2 papers with code
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models
Advances in language modeling have paved the way for novel human-AI co-writing experiences.
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.
Procrustes: a Dataflow and Accelerator for Sparse Deep Neural Network Training
The success of DNN pruning has led to the development of energy-efficient inference accelerators that support pruned models with sparse weight and activation tensors.
Full deep neural network training on a pruned weight budget
We introduce a DNN training technique that learns only a fraction of the full parameter set without incurring an accuracy penalty.
