3 papers
cs.IT2026
A New Class of Geometric Analog Error Correction Codes for Crossbar Based In-Memory Computing
Ziyuan Zhu, Changcheng Yuan, Ron M. Roth +2
Analog error correction codes have been proposed for analog in-memory computing on resistive crossbars, which can accelerate vector-matrix multiplication for machine learning. Unli…
cs.AR2025
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…
cs.AR2023
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…