Publication

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 given their overall higher efficiency. However, in databases, these methods have a hidden cost: the quantization of the real value into a smaller number is an expensive step. To address this issue, we present MLWeaving, a data structure and hardware acceleration technique intended to speed up learning of generalized linear models over low precision data. MLWeaving provides a compact in-memory representation that enables the retrieval of data at any level of precision. MLWeaving also provides a highly efficient implementation of stochastic gradient descent on FPGAs and enables the dynamic tuning of precision, instead of using a fixed precision level during learning. Experimental results show that MLWeaving converges up to 16 faster than low-precision implementations of first-order methods on CPUs.

@inproceedings{abc,
	abstract = {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 given their overall
higher efficiency. However, in databases, these methods have a
hidden cost: the quantization of the real value into a smaller number
is an expensive step. To address this issue, we present MLWeaving,
a data structure and hardware acceleration technique intended
to speed up learning of generalized linear models over low
precision data. MLWeaving provides a compact in-memory representation
that enables the retrieval of data at any level of precision.
MLWeaving also provides a highly efficient implementation
of stochastic gradient descent on FPGAs and enables the dynamic
tuning of precision, instead of using a fixed precision level during
learning. Experimental results show that MLWeaving converges
up to 16 faster than low-precision implementations of first-order
methods on CPUs. },
	author = {Zeke Wang and Kaan Kara and  and Gustavo Alonso and Onur  Mutlu and Ce Zhang},
	booktitle = {Proceedings of the VLDB 2019},
	title = {Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning },
	url = {https://dl.acm.org/doi/10.14778/3317315.3317322},
	venue = {Los Angeles, CA, USA},
	year = {2019}
}