Search Results for author:
Found 18 papers, 11 papers with code
Mélange: Cost Efficient Large Language Model Serving by Exploiting GPU Heterogeneity
Within this space, we show that there is not a linear relationship between GPU cost and performance, and identify three key LLM service characteristics that significantly affect which GPU type is the most cost effective: model request size, request rate, and latency service-level objective (SLO).
Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks
We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task.
Ranked #1 on
Code Completion
on SAFIM
AST-T5: Structure-Aware Pretraining for Code Generation and Understanding
Large language models (LLMs) have made significant advancements in code-related tasks, yet many LLMs treat code as simple sequences, neglecting its structured nature.
Online Speculative Decoding
We develop a prototype of online speculative decoding based on online knowledge distillation and evaluate it using both synthetic and real query data on several popular LLMs.
Spatialyze: A Geospatial Video Analytics System with Spatial-Aware Optimizations
In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos.
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
This allows SlimFit to freeze up to 95% of layers and reduce the overall on-device GPU memory usage of transformer-based models such as ViT and BERT by an average of 2. 2x, across different NLP and CV benchmarks/datasets such as GLUE, SQuAD 2. 0, CIFAR-10, CIFAR-100 and ImageNet with an average degradation of 0. 2% in accuracy.
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers
This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5.
An Evaluation of Memory Optimization Methods for Training Neural Networks
To address the challenge, this paper summarizes the scenarios in which MOMs prove advantageous for model training.
ADELT: Transpilation Between Deep Learning Frameworks
We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks.
NumS: Scalable Array Programming for the Cloud
LSHS is a local search method which optimizes operator placement by minimizing maximum memory and network load on any given node within a distributed system.
GACT: Activation Compressed Training for Generic Network Architectures
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.
PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context
Creating effective visualization is an important part of data analytics.
Falx: Synthesis-Powered Visualization Authoring
Modern visualization tools aim to allow data analysts to easily create exploratory visualizations.
Human-Computer Interaction Programming Languages
New Directions in Cloud Programming
Nearly twenty years after the launch of AWS, it remains difficult for most developers to harness the enormous potential of the cloud.
Program Synthesis
Distributed, Parallel, and Cluster Computing
Databases
Operating Systems
Programming Languages
Learning Programmatic Idioms for Scalable Semantic Parsing
Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens.
Mapping Language to Code in Programmatic Context
To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class.
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention.
Ranked #1 on
SQL Parsing
on Restaurants
