11 citations · 25 across the 10 of their papers we have counts for
5 papers · 1 filter
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
Abhiroop Bhattacharjee, Abhishek Moitra, Youngeun Kim +2
In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & co…
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park +1
Federated Learning (FL) is a privacy-preserving distributed machine learning approach geared towards applications in edge devices. However, the problem of designing custom neural a…
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
Yuhang Li, Youngeun Kim, Hyoungseob Park +1
Spiking Neural Networks (SNNs) are recognized as the candidate for the next-generation neural networks due to their bio-plausibility and energy efficiency. Recently, researchers ha…
DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks
Duy-Thanh Nguyen, Abhiroop Bhattacharjee, Abhishek Moitra +1
With ever increasing depth and width in deep neural networks to achieve state-of-the-art performance, deep learning computation has significantly grown, and dot-products remain dom…
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
Compute In-Memory platforms such as memristive crossbars are gaining focus as they facilitate acceleration of Deep Neural Networks (DNNs) with high area and compute-efficiencies. H…