3 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
Multi-primitive in-memory computing for Monte Carlo tree search
Tergel Molom-Ochir, Benjamin F. Morris, Yintao He +6
Monte Carlo tree search (MCTS) enables artificial intelligence (AI) decision-making, but requires 55-300 W on conventional processors, limiting edge deployment. In-memory computing…
NL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference
Lei Zhao, Luca Buonanno, Archit Gajjar +9
Resistive Random Access Memory (RRAM) based in-memory computing (IMC) accelerators offer significant performance and energy advantages for deep neural networks (DNNs), but face thr…
RACE-IT: A Reconfigurable Analog Computing Engine for In-Memory Transformer Acceleration
Lei Zhao, Aishwarya Natarajan, Luca Buonanno +6
Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel in a wide range of machine learning tasks. However, processing these models demands significa…