96 citations · 215 across the 9 of their papers we have counts for
25 papers
Trends in Energy Estimates for Computing in AI/Machine Learning Accelerators, Supercomputers, and Compute-Intensive Applications
Sadasivan Shankar, Albert Reuther
We examine the computational energy requirements of different systems driven by the geometrical scaling law, and increasing use of Artificial Intelligence or Machine Learning (AI-M…
AI and ML Accelerator Survey and Trends
Albert Reuther, Peter Michaleas, Michael Jones +3
This paper updates the survey of AI accelerators and processors from past three years. This paper collects and summarizes the current commercial accelerators that have been publicl…
Hypersparse Network Flow Analysis of Packets with GraphBLAS
Tyler Trigg, Chad Meiners, Sandeep Pisharody +23
Internet analysis is a major challenge due to the volume and rate of network traffic. In lieu of analyzing traffic as raw packets, network analysts often rely on compressed network…
The MIT Supercloud Dataset
Siddharth Samsi, Matthew L Weiss, David Bestor +24
Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers a…
Survey of Machine Learning Accelerators
Albert Reuther, Peter Michaleas, Michael Jones +3
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 m…
Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid
Andrew C. Kirby, Siddharth Samsi, Michael Jones +3
A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computatio…