Search Results for author:
Found 24 papers, 9 papers with code
Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids
Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data.
Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs
The relentless advancement of artificial intelligence (AI) and machine learning (ML) applications necessitates the development of specialized hardware accelerators capable of handling the increasing complexity and computational demands.
ClusterFormer: Clustering As A Universal Visual Learner
This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER.
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks
Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.
TransFlow: Transformer as Flow Learner
Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement.
Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation
This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase.
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns.
Towards Real-Time Temporal Graph Learning
Prior work operates on pre-collected temporal graph data and is not designed to handle updates on a graph in real-time.
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns.
MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms
For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.
Towards Sparsification of Graph Neural Networks
In this paper, we utilize two state-of-the-art model compression methods (1) train and prune and (2) sparse training for the sparsification of weight layers in GNNs.
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm.
H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture
Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs.
GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm
To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs.
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
In this paper we propose a novel hardware accelerator for GCN inference, called I-GCN, that significantly improves data locality and reduces unnecessary computation.
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
This is because real-world graphs can be extremely large and sparse.
G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency
While end-to-end jointly optimizing GNNs and their accelerators is promising in boosting GNNs' inference efficiency and expediting the design process, it is still underexplored due to the vast and distinct design spaces of GNNs and their accelerators.
Binary Complex Neural Network Acceleration on FPGA
Deep complex networks (DCN), in contrast, can learn from complex data, but have high computational costs; therefore, they cannot satisfy the instant decision-making requirements of many deployable systems dealing with short observations or short signal bursts.
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Over the years, accelerating neural networks with quantization has been widely studied.
BCNN: Binary Complex Neural Network
Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network -- a novel network design that processes binary complex inputs and weights through complex convolution, but still can harvest the extraordinary computation efficiency of BNNs.
Comparison Lift: Bandit-based Experimentation System for Online Advertising
Second, by adapting experimental design to information acquired during the test, it reduces substantially the cost of experimentation to the advertiser.
AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing
Deep learning systems have been successfully applied to Euclidean data such as images, video, and audio.
Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments
The product is currently deployed on the advertising platform of JD. com, an eCommerce company and a publisher of digital ads in China.
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size.
