173 citations · 782 across the 61 of their papers we have counts for
6 papers · 1 filter
MLPerf Inference Benchmark
Vijay Janapa Reddi, Christine Cheng, David Kanter +44
Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organization…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
BPPSA: Scaling Back-propagation by Parallel Scan Algorithm
Shang Wang, Yifan Bai, Gennady Pekhimenko
In an era when the performance of a single compute device plateaus, software must be designed to scale on massively parallel systems for better runtime performance. However, in the…
Priority-based Parameter Propagation for Distributed DNN Training
Anand Jayarajan, Jinliang Wei, Garth Gibson +2
Data parallel training is widely used for scaling distributed deep neural network (DNN) training. However, the performance benefits are often limited by the communication-heavy par…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory
Hongyu Miao, Myeongjae Jeon, Gennady Pekhimenko +2
Stream analytics have an insatiable demand for memory and performance. Emerging hybrid memories combine commodity DDR4 DRAM with 3D-stacked High Bandwidth Memory (HBM) DRAM to meet…