Inventec

Applications

A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram

As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG.

Transition Motion Tensor: A Data-Driven Approach for Versatile and Controllable Agents in Physically Simulated Environments

This paper proposes the Transition Motion Tensor, a data-driven framework that creates novel and physically accurate transitions outside of the motion dataset.

CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion

In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments.

A Trainable Reconciliation Method for Hierarchical Time-Series

In this paper, we propose a new general, flexible, and easy-to-implement reconciliation strategy based on an encoder-decoder neural network.

Demystifying data and AI for manufacturing: case studies from a major computer maker

We create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches.

TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

In this paper, we propose a framework called Trust MAE to address the problem of product defect classification. Instead of relying on defective images that are difficult to collect and laborious to label, our framework can accept datasets with unlabeled images.

Domain-Generalized Textured Surface Anomaly Detection

By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing.

A Dense Tensor Accelerator with Data Exchange Mesh for DNN and Vision Workloads

We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads.

MERIT: Tensor transform for memory-efficient vision processing on parallel architectures

We propose a mathematical formulation which can be useful for transferring the parallel algorithm optimization knowledge across computing platforms.

GrateTile: Efficient Sparse Tensor Tiling for CNN Processing

We propose GrateTile, an efficient, hardware friendly data storage scheme for sparse CNN feature maps (activations).

A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram

As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG.

A Trainable Reconciliation Method for Hierarchical Time-Series

In this paper, we propose a new general, flexible, and easy-to-implement reconciliation strategy based on an encoder-decoder neural network.

Demystifying data and AI for manufacturing: case studies from a major computer maker

We create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches.

TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

In this paper, we propose a framework called Trust MAE to address the problem of product defect classification. Instead of relying on defective images that are difficult to collect and laborious to label, our framework can accept datasets with unlabeled images.

Domain-Generalized Textured Surface Anomaly Detection

By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing.

Transition Motion Tensor: A Data-Driven Approach for Versatile and Controllable Agents in Physically Simulated Environments

This paper proposes the Transition Motion Tensor, a data-driven framework that creates novel and physically accurate transitions outside of the motion dataset.

CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion

In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments.

A Dense Tensor Accelerator with Data Exchange Mesh for DNN and Vision Workloads

We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads.

MERIT: Tensor transform for memory-efficient vision processing on parallel architectures

We propose a mathematical formulation which can be useful for transferring the parallel algorithm optimization knowledge across computing platforms.

GrateTile: Efficient Sparse Tensor Tiling for CNN Processing

We propose GrateTile, an efficient, hardware friendly data storage scheme for sparse CNN feature maps (activations).