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Found 35 papers, 3 papers with code
Testing RadiX-Nets: Advances in Viable Sparse Topologies
The exponential growth of data has sparked computational demands on ML research and industry use.
Lincoln AI Computing Survey (LAICS) Update
Finally, a brief description of each of the new accelerators that have been added in the survey this year is included.
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference
Large language models (LLMs) have exploded in popularity due to their new generative capabilities that go far beyond prior state-of-the-art.
Are ChatGPT and Other Similar Systems the Modern Lernaean Hydras of AI?
The rise of Generative Artificial Intelligence systems ("AI systems") has created unprecedented social engagement.
AI Enabled Maneuver Identification via the Maneuver Identification Challenge
Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators.
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
The MIT Supercloud Workload Classification Challenge
We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches.
Naming Schema for a Human Brain-Scale Neural Network
Deep neural networks have become increasingly large and sparse, allowing for the storage of large-scale neural networks with decreased costs of storage and computation.
AI Accelerator Survey and Trends
Over the past several years, new machine learning accelerators were being announced and released every month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications.
Maneuver Identification Challenge
The Maneuver Identification Challenge hosted at maneuver-id. mit. edu provides thousands of trajectories collected from pilots practicing in flight simulators, descriptions of maneuvers, and examples of these maneuvers performed by experienced pilots.
The MIT Supercloud Dataset
In this paper we introduce the MIT Supercloud Dataset which aims to foster innovative AI/ML approaches to the analysis of large scale HPC and datacenter/cloud operations.
Mathematics of Digital Hyperspace
The GraphBLAS standard currently supports hypergraphs, hypersparse matrices, the mathematics required for semilinks, and seamlessly performs graph, network, and matrix operations.
Survey of Machine Learning Accelerators
New machine learning accelerators are being announced and released each month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications.
Accuracy and Performance Comparison of Video Action Recognition Approaches
Over the past few years, there has been significant interest in video action recognition systems and models.
Benchmarking network fabrics for data distributed training of deep neural networks
The large computational requirements for training deep models have necessitated the development of new methods for faster training.
Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid
A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computational kernel execution on GPUs.
GraphChallenge.org Sparse Deep Neural Network Performance
The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems.
GraphChallenge.org Triangle Counting Performance
In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations.
Distributed, Parallel, and Cluster Computing Performance
Sparse Deep Neural Network Graph Challenge
The Sparse DNN Challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment.
Survey and Benchmarking of Machine Learning Accelerators
Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications.
Performance B.8; C.4
Securing HPC using Federated Authentication
Federated authentication can drastically reduce the overhead of basic account maintenance while simultaneously improving overall system security.
Distributed, Parallel, and Cluster Computing Cryptography and Security
Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M
This work describes the design and performance optimization of an implementation of hierarchical associative arrays that reduces memory pressure and dramatically increases the update rate into an associative array.
AI Enabling Technologies: A Survey
Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action.
RadiX-Net: Structured Sparse Matrices for Deep Neural Networks
We further present a functional-analytic conjecture based on the longstanding observation that sparse neural network topologies can attain the same expressive power as dense counterparts
A Billion Updates per Second Using 30,000 Hierarchical In-Memory D4M Databases
Streaming updates to a large associative array requires a hierarchical implementation to optimize the performance of the memory hierarchy.
Databases Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Networking and Internet Architecture
Training Behavior of Sparse Neural Network Topologies
We test pruning-based topologies, which are derived from an initially dense network whose connections are pruned, as well as RadiX-Nets, a class of network topologies with proven connectivity and sparsity properties.
Uncertainty Propagation in Deep Neural Networks Using Extended Kalman Filtering
Extended Kalman Filtering (EKF) can be used to propagate and quantify input uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the input distribution.
Neural Network Topologies for Sparse Training
The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them.
TabulaROSA: Tabular Operating System Architecture for Massively Parallel Heterogeneous Compute Engines
In this context, an operating system can be viewed as software that brokers and tracks the resources of the compute engines and is akin to a database management system.
Distributed, Parallel, and Cluster Computing Databases Operating Systems Performance
Sparse Deep Neural Network Exact Solutions
This work uses associative array DNNs to construct exact solutions and corresponding perturbation models to the rectified linear unit (ReLU) DNN equations that can be used to construct test vectors for sparse DNN implementations over various precisions.
Static Graph Challenge: Subgraph Isomorphism
The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms
Enabling Massive Deep Neural Networks with the GraphBLAS
The performance of the GraphBLAS implementation is measured relative to a standard dense linear algebra library implementation.
Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor
Thus, the performance of these applications on KNL systems is of high interest to LLSC users and the broader data analysis and machine learning communities.
Performance Instrumentation and Methods for Astrophysics Distributed, Parallel, and Cluster Computing Computational Physics
Non-Negative Matrix Factorization Test Cases
Non-negative matrix factorization (NMF) is a prob- lem with many applications, ranging from facial recognition to document clustering.
Large Enforced Sparse Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) is a common method for generating topic models from text data.
