15 citations · 20 across the 3 of their papers we have counts for
3 papers
Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models
Nathan Leroux, Paul-Philipp Manea, Chirag Sudarshan +4
Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoid…
A Framework for Formal Verification of DRAM Controllers
Lukas Steiner, Chirag Sudarshan, Matthias Jung +2
The large number of recent JEDEC DRAM standard releases and their increasing feature set makes it difficult for designers to rapidly upgrade the memory controller IPs to each new s…
eBrainII: A 3 kW Realtime Custom 3D DRAM integrated ASIC implementation of a Biologically Plausible Model of a Human Scale Cortex
Dimitrios Stathis, Chirag Sudarshan, Yu Yang +6
The Artificial Neural Networks (ANNs) like CNN/DNN and LSTM are not biologically plausible and in spite of their initial success, they cannot attain the cognitive capabilities enab…