16 citations · 29 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G
Qunsong Zeng, Jiawei Liu, Mingrui Jiang +7
To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-ef…
X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs
Giacomo Pedretti, John Moon, Pedro Bruel +11
Structured, or tabular, data is the most common format in data science. While deep learning models have proven formidable in learning from unstructured data such as images or speec…