TransPimLib: Efficient Transcendental Functions for Processing-in-Memory Systems
METADATA ONLY
Loading...
Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Processing-in-memory (PIM) promises to alleviate the data movement bottleneck in modern computing systems. However, current real-world PIM systems have the inherent disadvantage that their hardware is more constrained than in conventional processors (CPU, GPU), due to the difficulty and cost of building processing elements near or inside the memory. As a result, general-purpose PIM architectures support fairly limited instruction sets and struggle to execute complex operations such as transcendental functions and other hard-to-calculate operations (e.g., square root). These operations are particularly important for some modern workloads, e.g., activation functions in machine learning applications. In order to provide support for transcendental (and other hardto-calculate) functions in general-purpose PIM systems, we present TransPimLib, a library that provides CORDIC-based and LUT-based methods for trigonometric functions, hyperbolic functions, exponentiation, logarithm, square root, etc. We develop an implementation of TransPimLib for the UPMEM PIM architecture and perform a thorough evaluation of TransPimLib’s methods in terms of performance and accuracy, using microbenchmarks and three full workloads (Blackscholes, Sigmoid, Softmax). We open-source all our code and datasets at https://github.com/CMU-SAFARI/transpimlib.
Permanent link
Publication status
published
Editor
Book title
2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
Journal / series
Volume
Pages / Article No.
235 - 247
Publisher
IEEE
Event
2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
processing-in-memory; processing-near-memory; transcendental functions; activation functions; machine learning
Organisational unit
09483 - Mutlu, Onur / Mutlu, Onur