394 citations · 526 across the 12 of their papers we have counts for
12 papers
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Zhaoxia, Deng, Jongsoo Park +17
Tremendous success of machine learning (ML) and the unabated growth in ML model complexity motivated many ML-specific designs in both CPU and accelerator architectures to speed up…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models
Sihuan Li, Jianyu Huang, Ping Tak Peter Tang +4
Soft error, namely silent corruption of signal or datum in a computer system, cannot be caverlierly ignored as compute and communication density grow exponentially. Soft error dete…
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference
Daya Khudia, Jianyu Huang, Protonu Basu +4
Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that com…
Mixed-Precision Embedding Using a Cache
Jie Amy Yang, Jianyu Huang, Jongsoo Park +2
In recommendation systems, practitioners observed that increase in the number of embedding tables and their sizes often leads to significant improvement in model performances. Give…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi +21
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks…
Implementing Strassen's Algorithm with CUTLASS on NVIDIA Volta GPUs
Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn
Conventional GPU implementations of Strassen's algorithm (Strassen) typically rely on the existing high-performance matrix multiplication (GEMM), trading space for time. As a resul…
Strassen's Algorithm for Tensor Contraction
Jianyu Huang, Devin A. Matthews, Robert A. van de Geijn
Tensor contraction (TC) is an important computational kernel widely used in numerous applications. It is a multi-dimensional generalization of matrix multiplication (GEMM). While S…
Generating Families of Practical Fast Matrix Multiplication Algorithms
Jianyu Huang, Leslie Rice, Devin A. Matthews +1
Matrix multiplication (GEMM) is a core operation to numerous scientific applications. Traditional implementations of Strassen-like fast matrix multiplication (FMM) algorithms often…
BLISlab: A Sandbox for Optimizing GEMM
Jianyu Huang, Robert A. van de Geijn
Matrix-matrix multiplication is a fundamental operation of great importance to scientific computing and, increasingly, machine learning. It is a simple enough concept to be introdu…
Implementing Strassen's Algorithm with BLIS
Jianyu Huang, Tyler M. Smith, Greg M. Henry +1
We dispel with "street wisdom" regarding the practical implementation of Strassen's algorithm for matrix-matrix multiplication (DGEMM). Conventional wisdom: it is only practical fo…