Add to ORKGBoolean matrix multiplication (BMM) is a fundamental problem with applications in graph theory, group testing, data compression, and digital signal processing (DSP). The search for efficient BMM algorithms has produced several fast, albeit impractical, algorithms with sub-cubic time complexity. In this paper, we propose a memristor-crossbar framework for computing BMM at the hardware level in linear time. Our design leverages the diode-like characteristics of recently studied rectifying memristors to resolve the pervasive sneak paths constraint that is ubiquitous in crossbar computing
Since the fabrication of nanoscale memristors by HP Labs in 2008, there has been a sustained interes...
The Memristor is a newly synthesized circuit element correlating differences in electrical charge an...
The demise of Moore\u27s law, breakdown of Dennard Scaling, dark silicon phenomenon, process variati...
We are quickly reaching an impasse to the number of transistors that can be squeezed onto a single c...
Energy concerns, the infamous memory wall, and the enormous data deluge of the current big-data age ...
With Moore\u27s law approaching physical limitations of transistor size, researchers have started ex...
The recent emerging memristor can provide non-volatile memory storage but also intrinsic computing f...
The rise of data-intensive computational loads has exposed the processor-memory bottleneck in Von Ne...
Problems involving discrete Markov Chains are solved mathematically using matrix methods. Recently, ...
Memristive crossbar arrays can be used to realize matrix-vector multiplication (MVM) operations in c...
Alternatives to CMOS logic circuit implementations are under research for future scaled electronics....
Modern neuromorphic deep learning techniques, as well as unsupervised techniques like the locally co...
One of the most important constraints of today’s architectures for data-intensive applications is th...
Memristive crossbar arrays can be used to realize () operations in constant time complexity by explo...
Linear programming is required in a wide variety of application including routing, ...
Since the fabrication of nanoscale memristors by HP Labs in 2008, there has been a sustained interes...
The Memristor is a newly synthesized circuit element correlating differences in electrical charge an...
The demise of Moore\u27s law, breakdown of Dennard Scaling, dark silicon phenomenon, process variati...
We are quickly reaching an impasse to the number of transistors that can be squeezed onto a single c...
Energy concerns, the infamous memory wall, and the enormous data deluge of the current big-data age ...
With Moore\u27s law approaching physical limitations of transistor size, researchers have started ex...
The recent emerging memristor can provide non-volatile memory storage but also intrinsic computing f...
The rise of data-intensive computational loads has exposed the processor-memory bottleneck in Von Ne...
Problems involving discrete Markov Chains are solved mathematically using matrix methods. Recently, ...
Memristive crossbar arrays can be used to realize matrix-vector multiplication (MVM) operations in c...
Alternatives to CMOS logic circuit implementations are under research for future scaled electronics....
Modern neuromorphic deep learning techniques, as well as unsupervised techniques like the locally co...
One of the most important constraints of today’s architectures for data-intensive applications is th...
Memristive crossbar arrays can be used to realize () operations in constant time complexity by explo...
Linear programming is required in a wide variety of application including routing, ...
Since the fabrication of nanoscale memristors by HP Labs in 2008, there has been a sustained interes...
The Memristor is a newly synthesized circuit element correlating differences in electrical charge an...
The demise of Moore\u27s law, breakdown of Dennard Scaling, dark silicon phenomenon, process variati...