Biomimetic Nanoscale Devices and

Architectures for Brain-Inspired Computing

and Artificial Intelligence

Rashmi Jha1 ,Alex Jones1, Sam Wenke1, Eric Herrmann1, Tony

Bailey1, Andrew Rush1, Manish Kumar2

1Department of Electrical Engineering and Computing Systems, University of

Cincinnati, Cincinnati, Ohio

rashmi.jha@uc.edu, 513-556-13612Department of Mechanical, Industrial, and Materials Engineering, University

of Cincinnati, Cincinnati, Ohio

Manish.kumar@uc.edu, 513-556-5311

Motivation

“Why would we want to mimic the brain

when computers can already perform

computations at an incredibly fast speeds?”

vs

Digital Computing Limitations

• Quantity of data recorded

– In general, many businesses already have more data than they

know what to do with.

– Internet of Things will create millions of new devices that will add

significantly more data to process.

• Complexity of data

– Time-series data and high-dimensionality data are computationally

expensive.

• Algorithms to interpret data

– How to we decide what information is useful?

Limitations of Currently Available

Machine Learning: Deep Neural Network

(DNN)

DNN on conventional computing architecture are

compute intensive, power hungry, need a large set

of training data , and are trained to solve just some

specific sets of problems.

Goals: Replication vs. Mimicry

• We don’t need to completely replicate the functionality of the brain,

we already have billions of those.

• Rather, we need can identify the core components of the brain can

learn and identify features, we can create new architectures that

optimize that operation.

Neuron Operation and Action Potential Firing

Synapse

A Mechanism for Learning

C. Zamarreño-Ramos et al, "On spike-timing-dependent-plasticity,

memristive devices, and building a self-learning visual cortex," Frontiers

in Neuroscience, vol. 5, pp. 26, 2011.

Sensory Signal Processing

Temperature,

odor etc.

(Effector Cells)

Central Nervous

System

Sensory Information Encoding

Sensory Neurons in Silicon

Axon-Hillock Circuit, proposed by Prof. Carver Mead,

1980’sIndiveri et. al., Frontiers in Neuroscience, 2011

Synaptic Crossbar Structure

• Ultra Low-power

• Scalable (n2 synapses per 2n neurons)

• High endurance and reliability

Neuron

Synapse

Synapse Design Parameters:

• Connective weight between neurons

• Forward propagation w/o weight change

• Modify weight based on the pre/post spike

patterns

• Prevent Overfitting

Potential Synapse Realizations

C. D. Schuman et al, "A Survey of Neuromorphic Computing and Neural Networks in

Hardware," 2017.

Memristor:

• Stores memory state in its

resistance similar to charge

in a capacitor

• Synaptic Weight =

Resistance

• 4F2 device density

• Operate @ sub-µA currents

& relatively low voltages

Synaptic Memory Device

TiN

SrTiO3

W

-

+

T. J. Bailey and R. Jha, "Characterization of transient redox

dynamics in SrTiO3 synaptic devices," in 2017.

State of the Art in Inferencing IBM’s TrueNorth

• 1 million VLSI neurons

• SRAM synapses

• Weights trained offline and

programmed onto the board

• Real-time inferencing of images

Google’s “Tensor Processing

Unit” (TPU)

• ASIC to optimize tensor

operations.

• 8-bit multiply-and-add on

signed and unsigned integers.

• Voice recognition processing

and inferencing.

Neuromorphic Computing for

Robotic Navigation in Space

Current AI Software on Curiosity:

AEGIS – Target Identifying Algorithm

OASIS - Autonomous Science Framework

Opportunity to embed artificial intelligence directly onto robots to increase the

speed of learning and/or decision making.

Robot Navigation with

Neuromimetic Crossbar Simulation

,Gianluca et. al., IEEE Transactions on Robotics 21.5 (2005): 994-1004.

Robot Navigation Results

1 2 3

4 5 6

o : start | × : target

Conclusions• We established the reasoning to mimic the brain in order to create

new computing paradigms to compensate for limitations in digital

computing.

• The core elements and mechanisms behind learning in the brain

were elaborated on and proven realizations of neuron and synaptic

elements were presented.

• We established potential for having embedded artificial intelligence

for robots based on emerging neuromorphic devices.

• A successful robotic pathfinding application was demonstrated using

unsupervised learning scheme to guide the robot using local

knowledge of obstacles.

• Our approach is projected to be energy-efficient and scalable for

implementation on robotic systems.

• Future work is targeted towards the integration of neuron and

synaptic elements on-chip to achieve unsupervised learning in

hardware.

Acknowledgement• This project is currently supported by National Science

Foundation under CAREER (Award # 1556294).

• We would like to thank Dr. Mark Ritter and his group at IBM TJ Watson Research Center.

• We would like to thank our collaborators Dr. GennadiBersuker (Sematech), Dr. David Gilmer (Sematech), Dr. Prashant Majhi (Intel), Dr. Kevin Leedy (AFRL), Dr. Marc Cahay (U. of Cincinnati), Dr. Ali Minai ( U. of Cincinnati), Dr. Swaroop Ghosh (USF), Dr. Scott Molitor (U.Toledo), Dr. Cory Merkel (AFRL), Dr. Matt Casto (AFRL), Dr. Brian Dupiax (AFRL), Mr. Clare Thiem (ARFL).

Thank You!

Questions and Suggestions?