571 citations · 858 across the 10 of their papers we have counts for
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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…
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…
Dictionary Learning by Dynamical Neural Networks
Tsung-Han Lin, Ping Tak Peter Tang
A dynamical neural network consists of a set of interconnected neurons that interact over time continuously. It can exhibit computational properties in the sense that the dynamical…
Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results
Ping Tak Peter Tang, Tsung-Han Lin, Mike Davies
In a spiking neural network (SNN), individual neurons operate autonomously and only communicate with other neurons sparingly and asynchronously via spike signals. These characteris…
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal +2
The stochastic gradient descent (SGD) method and its variants are algorithms of choice for many Deep Learning tasks. These methods operate in a small-batch regime wherein a fractio…