no code implementations • 14 May 2022 • Yang Ni, Danny Abraham, Mariam Issa, Yeseong Kim, Pietro Mercati, Mohsen Imani
QHD provides real-time learning by further decreasing the memory capacity and the batch size.
no code implementations • 17 Feb 2022 • Jisung Park, Jeoggyun Kim, Yeseong Kim, Sungjin Lee, Onur Mutlu
Data reduction in storage systems is becoming increasingly important as an effective solution to minimize the management cost of a data center.
no code implementations • 1 Oct 2021 • Zhuowen Zou, Haleh Alimohamadi, Farhad Imani, Yeseong Kim, Mohsen Imani
Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning.
no code implementations • 20 Jul 2020 • Behnam Khaleghi, Sahand Salamat, Anthony Thomas, Fatemeh Asgarinejad, Yeseong Kim, Tajana Rosing
In this paper, we propose SHEARer, an algorithm-hardware co-optimization to improve the performance and energy consumption of HD computing.
no code implementations • 15 Jun 2018 • Mohsen Imani, Mohammad Samragh, Yeseong Kim, Saransh Gupta, Farinaz Koushanfar, Tajana Rosing
To enable in-memory processing, RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations, i. e., multiplication, addition, activation functions, and pooling.