203 citations · 1.3k across the 57 of their papers we have counts for
61 papers
STAR: Astrocyte-Inspired State-Augmented Repair for Supervised Memristive AI Hardware Systems
Yusuf Ahmed Khan, Zhuangyu Han, Abhronil Sengupta
Memristive crossbar arrays have emerged as a promising platform for efficient on-chip learning, enabling local learning rules such as Equilibrium Propagation (EP) to be realized wi…
Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain
Zhuangyu Han, Abhronil Sengupta
Reinforcement learning (RL) has enabled robust quadruped locomotion over complex terrain, but most learned controllers are trained offline with backpropagation in massively paralle…
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski +28
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Sci…
Bayesian Optimization of Crossbar-Based Compute-In-Memory System Design for Efficient DNN Inference
Arnob Saha, Bibhas Manna, Nikhil Kotikalapudi +4
Leveraging the high density and energy efficiency of Compute-In-Memory (CIM) crossbar-based Deep Neural Network (DNN) accelerators requires optimal Design Space Exploration (DSE),…
Trilinear Compute-in-Memory Architecture for Energy-Efficient Transformer Acceleration
Md Zesun Ahmed Mia, Jiahui Duan, Kai Ni +1
Self-attention in Transformers generates dynamic operands that force conventional Compute-in-Memory (CIM) accelerators into costly non-volatile memory (NVM) reprogramming cycles, d…
RMAAT: Astrocyte-Inspired Memory Compression and Replay for Efficient Long-Context Transformers
Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta
The quadratic complexity of self-attention mechanism presents a significant impediment to applying Transformer models to long sequences. This work explores computational principles…