16 citations · 29 across the 14 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…
A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory
Paul-Philipp Manea, Aishwarya Natarajan, Jim Ignowski +2
Analog content-addressable memories (aCAMs) based on memristors provide a promising pathway toward energy-efficient large-scale associative computing for Edge AI and embedded intel…
Memristive tabular variational autoencoder for compression of analog data in high energy physics
Rajat Gupta, Yuvaraj Elangovan, Tae Min Hong +5
We present an implementation of edge AI to compress data on an in-memory analog content-addressable memory (ACAM) device. A variational autoencoder is trained on a simulated sample…