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Found 11 papers, 2 papers with code
GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors
Heterogeneous hardware like Gaudi processor has been developed to enhance computations, especially matrix operations for Transformer-based large language models (LLMs) for generative AI tasks.
CityLLaVA: Efficient Fine-Tuning for VLMs in City Scenario
In the vast and dynamic landscape of urban settings, Traffic Safety Description and Analysis plays a pivotal role in applications ranging from insurance inspection to accident prevention.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators
However, the comparative performance of these AI accelerators on large language models has not been previously studied.
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks.
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors
Thirdly, we evaluate the performance of Transformers on GAUDI, particularly in handling long sequences and uncovering performance bottlenecks.
Transfer Learning Across Heterogeneous Features For Efficient Tensor Program Generation
Tuning tensor program generation involves searching for various possible program transformation combinations for a given program on target hardware to optimize the tensor program execution.
Degrees of Freedom and Information Criteria for the Synthetic Control Method
We provide an analytical characterization of the model flexibility of the synthetic control method (SCM) in the familiar form of degrees of freedom.
Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation
Evaluating with 20, 480 input problems, we show that Smartfluidnet achieves 1. 46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.
Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation
In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution.
FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices.
