Search Results for author:
Found 6 papers, 2 papers with code
Accelerating Production LLMs with Combined Token/Embedding Speculators
This technical report describes the design and training of novel speculative decoding draft models, for accelerating the inference speeds of large language models in a production environment.
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
To incorporate sufficient diversity into the IoT training data, one therefore needs to consider a combinatorial explosion of training cases that are multiplicative in the number of objects considered and the possible environmental conditions in which such objects may be encountered.
TP-Aware Dequantization
In this paper, we present a novel method that reduces model inference latency during distributed deployment of Large Language Models (LLMs).
Accelerating a Triton Fused Kernel for W4A16 Quantized Inference with SplitK work decomposition
Our implementation shows improvement for the type of skinny matrix-matrix multiplications found in foundation model inference workloads.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
