63 citations · 98 across the 5 of their papers we have counts for
4 papers · 1 filter
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
Gokul Krishnan, Sumit K. Mandal, Manvitha Pannala +4
In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes.…
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Adnan Siraj Rakin, Li Yang, Jingtao Li +5
Recently developed adversarial weight attack, a.k.a. bit-flip attack (BFA), has shown enormous success in compromising Deep Neural Network (DNN) performance with an extremely small…
Structural Pruning in Deep Neural Networks: A Small-World Approach
Gokul Krishnan, Xiaocong Du, Yu Cao
Deep Neural Networks (DNNs) are usually over-parameterized, causing excessive memory and interconnection cost on the hardware platform. Existing pruning approaches remove secondary…
Automatic Compiler Based FPGA Accelerator for CNN Training
Shreyas Kolala Venkataramanaiah, Yufei Ma, Shihui Yin +4
Training of convolutional neural networks (CNNs)on embedded platforms to support on-device learning is earning vital importance in recent days. Designing flexible training hard-war…