activity
20162026
most citedDark Memory and Accelerator-Rich System Optimization in the Dark Silicon Era

128 citations · 460 across the 49 of their papers we have counts for

collaborators

51 papers

cs.NE2026

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…

cond-mat.str-el2026

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…

cs.ET2026★ 2 cited

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…

cs.NE2025

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…

cs.ET2025

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…

cs.AR2024

IMPACT:InMemory ComPuting Architecture Based on Y-FlAsh Technology for Coalesced Tsetlin Machine Inference

Omar Ghazal, Wei Wang, Shahar Kvatinsky +3

The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann archi…

cs.AR2024

Accelerating DNA Read Mapping with Digital Processing-in-Memory

Rotem Ben-Hur, Orian Leitersdorf, Ronny Ronen +5

Genome analysis has revolutionized fields such as personalized medicine and forensics. Modern sequencing machines generate vast amounts of fragmented strings of genome data called…

cs.AR2024★ 6 cited

VVTEAM: A Compact Behavioral Model for Volatile Memristors

Tanay Patni, Rishona Daniels, Shahar Kvatinsky

Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over…

cs.AR2024★ 3 cited

Bitwise Logic Using Phase Change Memory Devices Based on the Pinatubo Architecture

Noa Aflalo, Eilam Yalon, Shahar Kvatinsky

This paper experimentally demonstrates a near-crossbar memory logic technique called Pinatubo. Pinatubo, an acronym for Processing In Non-volatile memory ArchiTecture for bUlk Bitw…

cs.AR2024★ 4 cited

Assessing the Performance of Stateful Logic in 1-Selector-1-RRAM Crossbar Arrays

Arjun Tyagi, Shahar Kvatinsky

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their…

eess.SP2024★ 3 cited

Roadmap to Neuromorphic Computing with Emerging Technologies

Adnan Mehonic, Daniele Ielmini, Kaushik Roy +50

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently util…

eess.SY2024

A Pipelined Memristive Neural Network Analog-to-Digital Converter

Loai Danial, Kanishka Sharma, Shahar Kvatinsky

With the advent of high-speed, high-precision, and low-power mixed-signal systems, there is an ever-growing demand for accurate, fast, and energy-efficient analog-to-digital (ADCs)…

cond-mat.supr-con2024

Low-power Rapid Planar Superconducting Logic Devices

Nikolay Gusarov, Rajesh Mandal, Issa Salameh +3

The rapid-pace growing demand for high-performance computation and big-data manipulation entails substantial increase in global power consumption, and challenging thermal managemen…

cs.AR2024★ 1 cited

Transimpedance Amplifier with Automatic Gain Control Based on Memristors for Optical Signal Acquisition

Sariel Hodisan, Shahar Kvatinsky

Transimpedance amplifiers (TIA) play a crucial role in various electronic systems, especially in optical signal acquisition. However, their performance is often hampered by saturat…

cs.CR2023★ 2 cited

TDPP: Two-Dimensional Permutation-Based Protection of Memristive Deep Neural Networks

Minhui Zou, Zhenhua Zhu, Tzofnat Greenberg-Toledo +6

The execution of deep neural network (DNN) algorithms suffers from significant bottlenecks due to the separation of the processing and memory units in traditional computer systems.…

cs.AR2023★ 8 cited

Experimental Demonstration of Non-Stateful In-Memory Logic with 1T1R OxRAM Valence Change Mechanism Memristors

Henriette Padberg, Amir Regev, Giuseppe Piccolboni +4

Processing-in-memory (PIM) is attractive to overcome the limitations of modern computing systems. Numerous PIM systems exist, varying by the technologies and logic techniques used.…

cs.AR2023

PyPIM: Integrating Digital Processing-in-Memory from Microarchitectural Design to Python Tensors

Orian Leitersdorf, Ronny Ronen, Shahar Kvatinsky

Digital processing-in-memory (PIM) architectures mitigate the memory wall problem by facilitating parallel bitwise operations directly within the memory. Recent works have demonstr…

cs.CR2023★ 30 cited

An Asynchronous and Low-Power True Random Number Generator using STT-MTJ

Ben Perach, Shahar Kvatinsky

The emerging Spin Transfer Torque Magnetic Tunnel Junction (STT-MTJ) technology exhibits interesting stochastic behavior combined with small area and low operation energy. It is, t…

cs.DB2023

Accelerating Relational Database Analytical Processing with Bulk-Bitwise Processing-in-Memory

Ben Perach, Ronny Ronen, Shahar Kvatinsky

Online Analytical Processing (OLAP) for relational databases is a business decision support application. The application receives queries about the business database, usually reque…

cs.AR2023

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…

cs.AR2023★ 13 cited

FourierPIM: High-Throughput In-Memory Fast Fourier Transform and Polynomial Multiplication

Orian Leitersdorf, Yahav Boneh, Gonen Gazit +2

The Discrete Fourier Transform (DFT) is essential for various applications ranging from signal processing to convolution and polynomial multiplication. The groundbreaking Fast Four…

cs.LG2023

ClaPIM: Scalable Sequence CLAssification using Processing-In-Memory

Marcel Khalifa, Barak Hoffer, Orian Leitersdorf +4

DNA sequence classification is a fundamental task in computational biology with vast implications for applications such as disease prevention and drug design. Therefore, fast high-…

cs.AR2023★ 1 cited

Enabling Relational Database Analytical Processing in Bulk-Bitwise Processing-In-Memory

Ben Perach, Ronny Ronen, Shahar Kvatinsky

Bulk-bitwise processing-in-memory (PIM), an emerging computational paradigm utilizing memory arrays as computational units, has been shown to benefit database applications. This pa…

cs.ET2022★ 20 cited

Stateful Logic using Phase Change Memory

Barak Hoffer, Nicolás Wainstein, Christopher M. Neumann +3

Stateful logic is a digital processing-in-memory technique that could address von Neumann memory bottleneck challenges while maintaining backward compatibility with standard von Ne…