23 citations · 98 across the 32 of their papers we have counts for
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When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design
Abhishek Moitra, Abhiroop Bhattacharjee, Yuhang Li +2
This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highl…
TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing
Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim +1
Due to the high computation overhead of Vision Transformers (ViTs), In-memory Computing architectures are being researched towards energy-efficient deployment in edge-computing sce…
PIVOT- Input-aware Path Selection for Energy-efficient ViT Inference
Abhishek Moitra, Abhiroop Bhattacharjee, Priyadarshini Panda
The attention module in vision transformers(ViTs) performs intricate spatial correlations, contributing significantly to accuracy and delay. It is thereby important to modulate the…
ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation
Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda
Transformers have revolutionized various real-world applications from natural language processing to computer vision. However, traditional von-Neumann computing paradigm faces memo…
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Donghyun Lee, Ruokai Yin, Youngeun Kim +3
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation. How…