23 citations · 58 across the 5 of their papers we have counts for
7 papers
Stochastic Gradient Descent without Full Data Shuffle
Lijie Xu, Shuang Qiu, Binhang Yuan +9
Stochastic gradient descent (SGD) is the cornerstone of modern machine learning (ML) systems. Despite its computational efficiency, SGD requires random data access that is inherent…
HyperLogLog Sketch Acceleration on FPGA
Amit Kulkarni, Monica Chiosa, Thomas B. Preußer +3
Data sketches are a set of widely used approximated data summarizing techniques. Their fundamental property is sub-linear memory complexity on the input cardinality, an important a…
High Bandwidth Memory on FPGAs: A Data Analytics Perspective
Kaan Kara, Christoph Hagleitner, Dionysios Diamantopoulos +2
FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the ensuing necessity for specialization in hardware. Driven by thi…
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)
Zeke Wang, Kaan Kara, Hantian Zhang +3
Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest…
Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications
Nezihe Merve Gürel, Kaan Kara, Alen Stojanov +5
Modern scientific instruments produce vast amounts of data, which can overwhelm the processing ability of computer systems. Lossy compression of data is an intriguing solution, but…
Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond
Heng Guo, Kaan Kara, Ce Zhang
For Markov chain Monte Carlo methods, one of the greatest discrepancies between theory and system is the scan order - while most theoretical development on the mixing time analysis…
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
Hantian Zhang, Jerry Li, Kaan Kara +3
Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order…