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Deep Learning Accelerators (DLAs) are effective to improve both performance and energy efficiency of compute-intensive deep learning algorithms. A flexible and portable mean to exploit DLAs is using high-performance software libraries with well-...

Support for Machine Learning (ML) applications in networking has significantly improved over the last decade. The availability of public datasets and programmable switching fabrics (including low-level languages to program them) presents a full-stack ...

Jointly utilizing multiple GPUs to train graph neural networks (GNNs) is crucial for handling large graphs and achieving high efficiency. However, we find that existing systems suffer from high communication costs and low GPU utilization due to improper ...

Temporal Graph Neural Networks are gaining popularity in modeling interactions on dynamic graphs. Among them, Temporal Graph Attention Networks (TGAT) have gained adoption in predictive tasks, such as link prediction, in a range of application domains. ...

Tucker decomposition is one of the SOTA CNN model compression techniques. However, unlike the FLOPs reduction, we observe very limited inference time reduction with Tucker-compressed models using existing GPU software such as cuDNN. To this end, we ...

We aim to automate decades of research and experience in register allocation, leveraging machine learning. We tackle this problem by embedding a multi-agent reinforcement learning algorithm within LLVM, training it with the state of the art ...

Loop interchange is an important code optimization that improves data locality and extracts parallelism. While previous research in compilers has tried to automate the selection of which loops to interchange, existing methods have an important ...

Auto-scheduling for tensor programs is a process where a search algorithm automatically explores candidate schedules (program transformations) for a given program on a target hardware platform to improve its performance. However this can be a very time ...

Weightless neural networks (WNNs) are a class of machine learning model which use table lookups to perform inference, rather than the multiply-accumulate operations typical of deep neural networks (DNNs). Individual weightless neurons are capable of ...

The Convolutional Neural Network (CNN) kernel is a fundamental building block for deep learning, which dominates the computational cost of deep learning pipelines for image analysis. The synthesis of high-performance GPU kernels for CNNs is thus of ...

Network slicing achieves cost-efficient slice customization to support heterogeneous applications and services. Configuring cross-domain resources to end-to-end slices based on service-level agreements, however, is challenging, due to the complicated ...

Deep Neural Networks (DNNs) have demonstrated impressive performance on many machine-learning tasks such as image recognition and language modeling, and are becoming prevalent even on mobile platforms. Despite so, designing neural architectures still ...

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never been realized ...

Communication overhead is one of the major obstacles to train large deep learning models at scale. Gradient sparsification is a promising technique to reduce the communication volume. However, it is very challenging to obtain real performance ...

Systems for training massive deep learning models (billions of parameters) today assume and require specialized "hyperclusters": hundreds or thousands of GPUs wired with specialized high-bandwidth interconnects such as NV-Link and Infiniband. Besides ...

An adaptive video object detection system selects different execution paths at runtime, based on video content and available resources, so as to maximize accuracy under a target latency objective (e.g., 30 frames per second). Such a system is well ...

Neural network training requires a large amount of computation and thus GPUs are often used for the acceleration. While they improve the performance, GPUs are underutilized during the training. This paper proposes out-of-order (ooo) back-prop, an ...

Reinforcement learning (RL) is emerging as a powerful technique for solving complex code optimization tasks with an ample search space. While promising, existing solutions require a painstaking manual process to tune the right task-specific RL ...

Training models with massive inputs is a significant challenge in the development of Deep Learning pipelines to process very large digital image datasets as required by Whole Slide Imaging (WSI) in computational pathology and analysis of brain fMRI ...

This work reveals that memory-intensive computation is a rising performance-critical factor in recent machine learning models. Due to a unique set of new challenges, existing ML optimizing compilers cannot perform efficient fusion under complex two-...