RACE-IT: A Reconfigurable Analog CAM-Crossbar Engine for In-Memory Transformer Acceleration

no code implementations • 29 Nov 2023 • Lei Zhao, Luca Buonanno, Ron M. Roth, Sergey Serebryakov, Archit Gajjar, John Moon, Jim Ignowski, Giacomo Pedretti

Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel in a wide range of machine learning tasks.

X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs

1 code implementation • 3 Apr 2023 • Giacomo Pedretti, John Moon, Pedro Bruel, Sergey Serebryakov, Ron M. Roth, Luca Buonanno, Tobias Ziegler, Cong Xu, Martin Foltin, Paolo Faraboschi, Jim Ignowski, Catherine E. Graves

In this work, we focus on an overall analog-digital architecture implementing a novel increased precision analog CAM and a programmable network on chip allowing the inference of state-of-the-art tree-based ML models, such as XGBoost and CatBoost.

One-step regression and classification with crosspoint resistive memory arrays

no code implementations • 5 May 2020 • Zhong Sun, Giacomo Pedretti, Alessandro Bricalli, Daniele Ielmini

Here we show a crosspoint resistive memory circuit with feedback configuration can execute linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory.

Classification General Classification +1