Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies

By Shahar Kvatinsky, Guy Satat, Nimrod Wald, Eby G. Friedman, Avinoam Kolodny, Uri C. Weiser. IEEE Transactions on Very Large Scale Integration (vlsi) Systems, 2014

611 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/160631daa8ecea247014401f5429deb49883d395

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Shahar Kvatinsky
2. Avinoam Kolodny
3. Guy Satat
4. Nimrod Wald
5. Eby G. Friedman
6. Uri C. Weiser

In Electrical and Electronic Engineering

1. Onur Mutlu
2. Shahar Kvatinsky
3. Avinoam Kolodny
4. Guy Satat
5. Nimrod Wald
6. Jeremie S. Kim
7. Uri C. Weiser
8. Eby G. Friedman

Citations in the corpus

• Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology (2017, background): “Similarly, recent works [63, 64, 65, 72] propose mechanisms to perform bitwise operations and other operations (3-bit full adder) completely inside a memristor array.”
• Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training (2015, neutral): “Note (14) is a reasonable assumption, as shown in [48].”
• MAGIC—Memristor-Aided Logic (2014, background): “Memristive-only logic gates within a memristive crossbar array reduce power and provide an opportunity for novel nonvon Neumann architectures, where the logical operations are executed within the memory [11].”
• Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “Similar substrates can perform simple arithmetic operations in SRAM [79, 80] and arithmetic and logical operations in memristors [81, 180, 181, 182, 183].”