8 papers
Performance Analysis of Digital Processing-in-Memory through a Case Study on Convolutional-Neural-Network Acceleration
Orian Leitersdorf, Ronny Ronen, Shahar Kvatinsky
Processing-in-Memory (PIM) architectures are evolving to minimize data movement by leveraging the same physical devices for both memory and logic functionalities. While analog PIM…
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
Rishona Daniels, Duna Wattad, Ronny Ronen +2
Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memri…
Harnessing the VO2 Phase Transition for Automatic Gain Control in Transimpedance Amplifiers
Amir Gildor, Sariel Hodisan, Shahar Kvatinsky +1
Transimpedance amplifiers (TIAs) are essential in sensor electronics, converting input currents into output voltages. Conventional TIAs utilize fixed-gain resistors, which saturate…
A Comparative Study of Digital Memristor-Based Processing-In-Memory from a Device and Reliability Perspective
Thomas Neuner, Henriette Padberg, Lior Kornblum +3
As data-intensive applications increasingly strain conventional computing systems, processing-in-memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by mi…
Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen +2
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout la…
Stateful Logic In-Memory Using Gain-Cell eDRAM
Barak Hoffer, Shahar Kvatinsky
Modern data-intensive applications demand memory solutions that deliver high-density, low-power, and integrated computational capabilities to reduce data movement overhead. This pa…