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Found 48 papers, 23 papers with code
xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem
In this paper, we use Clique Partition- ing Problem (CPP), which is an Integer Pro- gram (IP) to formulate ER as a graph partition- ing problem and then highlight the explainable nature of this method.
Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level
We observe that our fused kernels successfully circumvent some of the unavoidable inefficiencies in unfused implementations.
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU memory, storage, and GPU memory with a hybrid data placement strategy.
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data.
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs.
Submission-Aware Reviewer Profiling for Reviewer Recommender System
We propose an approach that learns from each abstract published by a potential reviewer the topics studied and the explicit context in which the reviewer studied the topics.
Can Language Models Be Specific? How?
We hope this work can bring to awareness the notion of specificity of language models and encourage the research community to further explore this important but understudied problem.
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships.
Understanding Jargon: Combining Extraction and Generation for Definition Modeling
From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge.
Graph Neural Network Training with Data Tiering
With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead.
MLHarness: A Scalable Benchmarking System for MLCommons
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common set of measures for ML/DL models with large scale open datasets under common development practices and resources so that people can benchmark and compare models quality and performance on a common ground.
Open Relation Modeling: Learning to Define Relations between Entities
Relations between entities can be represented by different instances, e. g., a sentence containing both entities or a fact in a Knowledge Graph (KG).
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach
To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain.
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric.
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help.
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step.
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space
This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.
TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes
MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications.
Distributed, Parallel, and Cluster Computing
Interpretable Visual Reasoning via Induced Symbolic Space
Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space.
Ranked #3 on
Visual Question Answering (VQA)
on CLEVR
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.
At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation
This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020.
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.
Ranked #8 on
Semantic Segmentation
on DensePASS
DLSpec: A Deep Learning Task Exchange Specification
Deep Learning (DL) innovations are being introduced at a rapid pace.
MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.
The Design and Implementation of a Scalable DL Benchmarking Platform
MLModelScope defines abstractions for frameworks and supports board range of DL models and evaluation scenarios.
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)
We show that DLBricks provides an accurate performance estimate for the DL models and reduces the benchmarking time across systems (e. g. within $95\%$ accuracy and up to $4. 4\times$ benchmarking time speedup on Amazon EC2 c5. xlarge).
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving
The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations.
Benanza: Automatic $μ$Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs
An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities.
MLModelScope: A Distributed Platform for ML Model Evaluation and Benchmarking at Scale
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers.
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Object detection and tracking are challenging tasks for resource-constrained embedded systems.
SPGNet: Semantic Prediction Guidance for Scene Parsing
The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path.
XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs
Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack).
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.
A Retrospective Recount of Computer Architecture Research with a Data-Driven Study of Over Four Decades of ISCA Publications
Recently we decided to conduct a more thorough study based on all past papers of International Symposium on Computer Architecture (ISCA) from 1973 to 2018, which resulted this article.
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices
Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML.
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge
While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus
Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style.
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
We also present the PUMA compiler which translates high-level code to PUMA ISA.
Emerging Technologies Hardware Architecture
Frustrated with Replicating Claims of a Shared Model? A Solution
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them.
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines.
Distributed, Parallel, and Cluster Computing
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization
While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
In particular, DCR places a separate classification network in parallel with the localization network (base detector).
SCOPE: C3SR Systems Characterization and Benchmarking Framework
This report presents the design of the Scope infrastructure for extensible and portable benchmarking.
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