



Joseph S. Friedman
Research Summary
I am an assistant professor of Electrical & Computer Engineering at The University of Texas at Dallas and director of the NeuroSpinCompute Laboratory.
My research objective is to invent, design, and analyze novel logical and neuromorphic computing paradigms that exploit nanoscale phenomena to achieve greater capabilities than conventional CMOS architectures.
In sharp contrast to other proposals for beyondCMOS and spintronic computing, the central theme of my research is to ensure that individual switching elements can be cascaded and integrated in efficient largescale information processing systems.

Magnetic domain wall neurons and synapses emulate the behavior of neurobiological elements through manipulation of magnetic domain walls.
The proposed artificial neurons are the first that intrinsically provide the leaking, integrating, firing, and lateral inhibition capabilities without any additional devices or circuitry.
This structure is used to perform handwritten digit recognition with 94% accuracy. 
This project aims to design and demonstrate an online learning circuit that leverages the stochastic switching of STTMRAM devices to enable onchip online learning and recognition. 

from Nature Communications 8, 15635 under CC BY 4.0 
In allcarbon spin logic (ACSL), graphene nanoribbons (GNRs) function as spindiodes connected by carbon nanotubes (CNTs) in accordance with spindiode logic (SDL). The exceptional properties of lowdimensional carbon, in concert with electromagnetic wavebased signal propagation, provide the potential for Terahertz operation and a 100x improvement in powerdelay product. 
Reversible skyrmion logic leverages magnetic skyrmions in the first nanoscale realization of conservative logic, providing a vision for energyefficient computation.
In this system, magnetic skyrmions propagate through a twodimensional ferromagnetic structure while performing reversible logic operations at the gate junctions.
A simple global clock enables direct cascading with the potential for scalable highspeed lowpower reversible Boolean and quantum computing. 


Bayesian inference is a powerful approach for integrating independent conflicting information for decisionmaking and robotics, performed with limited efficiency by generalpurpose computers.
Excitingly, Bayesian inference can be performed extremely efficiently through stochastic computing with Muller Celements.
This faulttolerant circuit structure enables naive Bayesian inference with multiple orders of magnitude decrease in AEDP. 
The nonvolatility of memristors enables stateful logic, in which bits are encoded as binary resistance states.
However, an alaysis of the required control circuit shows that when this overhead circuitry is included, stateful memristor logic is one billion times less efficient than conventional CMOS logic! 


Complementary MAgnetic Tunnel junction logic (CMAT) is a spintronic logic family enabling the cascading of MTJ gates.
With a complementary structure analogous to CMOS, CMAT provides nonvolatile logic that enables nonvon Neumann architectures. 
Spindiode logic (SDL) is a spintronic logic family in which twoterminal volatile magnetoresistive devices can be directly cascaded.
This logic family uses the current passing through the spindiodes as the source of the magnetic field to switch other spindiodes.
Positive and negative magnetoresistance devices can be cascaded in this manner to realize largescale computing systems. 


Threshold logic permits the efficient computation of complex multiinput functions, but the noise margins of electronic devices limit the input combinations to the resolution of the device.
The recently demonstrated fourgate electrostatically formed nanowires (EFNs) are natural candidates for threshold logic, enabling compact threshold logic circuits. 
