2 citations · 2 across the 4 of their papers we have counts for
5 papers
First Demonstration of Second-order Training of Deep Neural Networks with In-memory Analog Matrix Computing
Saitao Zhang, Yubiao Luo, Shiqing Wang +5
Second-order optimization methods, which leverage curvature information, offer faster and more stable convergence than first-order methods such as stochastic gradient descent (SGD)…
RRAM-Based Analog Matrix Computing for Massive MIMO Signal Processing: A Review
Pushen Zuo, Zhong Sun
Resistive random-access memory (RRAM) provides an excellent platform for analog matrix computing (AMC), enabling both matrix-vector multiplication (MVM) and the solution of matrix…
GRAMC: General-purpose and reconfigurable analog matrix computing architecture
Lunshuai Pan, Shiqing Wang, Pushen Zuo +1
In-memory analog matrix computing (AMC) with resistive random-access memory (RRAM) represents a highly promising solution that solves matrix problems in one step. However, the exis…
BlockAMC: Scalable In-Memory Analog Matrix Computing for Solving Linear Systems
Lunshuai Pan, Pushen Zuo, Yubiao Luo +2
Recently, in-memory analog matrix computing (AMC) with nonvolatile resistive memory has been developed for solving matrix problems in one step, e.g., matrix inversion of solving li…
Extremely-Fast, Energy-Efficient Massive MIMO Precoding with Analog RRAM Matrix Computing
Pushen Zuo, Zhong Sun, Ru Huang
Signal processing in wireless communications, such as precoding, detection, and channel estimation, are basically about solving inverse matrix problems, which, however, are slow an…