Neuromorphic Network Based on Carbon Nanotube/Polymer Composites

Transcription

1 Neuromorphic Network Based on Carbon Nanotube/Polymer Composites Andrew Tudor, Kyunghyun Kim, Alex Ming Shen, Chris Shaffer, Dongwon Lee, Cameron D. Danesh, and Yong Chen Department of Mechanical & Aerospace Engineering University of California, Los Angeles August 20, 2014

2 Could an airplane be driven by an artificial brain?

3 Brain Supercomputer Signal Processing Speed ~ 3x10 16 FLOPS ~ 2.57x10 15 FLOPS

4 Brain Supercomputer Signal Power Processing consumption Speed 4 x10 16 FLOPS ~ 30 W 2 x10 16 FLOPS ~10 6 W

5 Brain Supercomputer Signal Weight Processing Speed 4 x10 16 FLOPS ~ 1500 g 2 x10 16 FLOPS ~ 10 8 g

6 Brain Supercomputer Signal Processing Size Speed 4 x10 16 FLOPS ~ 0.15 m 2 x10 16 FLOPS ~ 10 2 m

7 Brain Supercomputer Logic Self-modified via learning in complex environments Programmed, by the brains after extensive tests in different environments

8 Neural Network Synapses Neurons The Brain The synapses has the functions of analog logic, memory, learning and operated at <1 nw. The neuronal network process signals in a massive parallel signal processing with a high speed. A brain is composed of a neural network with ~100 billion neurons and 100 trillion synapses (junctions between the neurons).

9 100 mm CNT network from inorganic composite Neural network from organic composite

10 Spiking Neuromorphic Integrated Circuit (SNIC) Polymer cap CNT network Source & drain PVDF Polymer Gate electrodes Si soma circuit

11 I DS [na] Spike Signal Processing in Synapstor V G [V] 30 x 10-8 B8P3, D6 (120715) Pulse Response (VI0V,VDSN0.5V) Gate Electrode PVDF Electric Dipole Holes Current 1.5 SiO Time Time [s] A voltage pulse applied on the gate of a synaptic transistor triggers electric dipoles in the ferroelectric polymer to generate post-synaptic current (PSC). Power consumption: 2.6 nw/synapstor.

12 Memory & Learning in Synapstor Gate Electrode PVDF Electric Dipole Holes The PSC amplitudes of the postsynaptic current can be configured to analog states quantitatively and reversibly by modifying the electrical dipoles in the ferroelectric polymer. I d is increased (decreased) versus the number of the negative (positive) the pulses.

13 Memory & Learning in Synapstor Gate Electrode PVDF Electric Dipole Holes The PSC amplitudes of the postsynaptic current can be configured to analog states quantitatively and reversibly by modifying the electrical dipoles in the ferroelectric polymer. I d is increased (decreased) versus the number of the negative (positive) the pulses.

14 Spiking Neuromorphic Integrated Network (SNIC) Spikes from the sensing network are input to network. i The spikes trigger postsynaptic currents via synapstors. The synapstors are modified in a real-time learning process. CNT Synapstor Si neurons The currents from synapstors flows into Si neurons, triggering output spikes. C-L CHEN, K. KIM, Q.TRUONG, A SHEN, AND Y. CHEN, A SPIKING NEURON CIRCUIT BASED ON ACARBON NANOTUBE TRANSISTOR, NANOTECHNOLOGY, 23(27), 1-6, (2012) Y. S. AHN, K. KIM, H. K. PARK, H. T. HAHN, AND Y. CHEN, FUNCTIONALIZED CARBON NANOTUBE NETWORKS WITH FIELD-TUNABLE BANDGAPS, ADVANCED MATERIALS, 23, , (2011) j

15 Learning to achieve the goal In the SNIC, the output spike rate y is spontaneously modified by following the equation, j F dy dt j h j ( y F) j h y j j : A positive transfer function. : The fluctuative component in y. F : Performance function. j y j ' y j

16 Dynamic Interaction between SNIC & UAV Sensors Interface x i ( t) UAV SNIC Actuators y j ( t) We have established a close-loop dynamic control between SNIC and a UAV. Sensing signals (images from a video) in the Drone are sent to SNIC. SNIC processes the sensing signals, and generates actuation signals to drive the Drone. The control algorithm in SNIC is also optimized via learning in real time. 16

17 SNIC learns how to drive a UAV to follow a target SNIC drives a UAV without learning DY DX

18 SNIC learns how to drive a UAV in the wind

19 CNT/Polymer Composite Neuromorphic Network CNT/Polymer Synapstor Spike Neuromorphic Intelligent Circuit (SNIC) Self-programmed SNIC to drive a UAV Invented a device, synapstor, based on CNT composites to emulate biological synapse with analog logic, memory, and learning functions, and a low power consumption (1 nw/spike). Developed a spike neuromorphic integrated circuit (SNIC) based on CNT composites with high-speed parallel signal processing, low power consumption (1 mw), and spontaneous learning capability. Demonstrated the first composite neuromorphic network with self-program and learning functions to drive UAV in a dynamically changing environment.

20 Speed (FLOPS) Long way to go The brain SNIC (~10 12 synapstors) Tianhe PC SNIC (10 3 synapstors) SNIC (4 synapstors) SNIC with ~10 12 synapstors could have a signal processing speed exceeding a supercomputer, a power consumption of 100 W, a size of 10 cm, and most importantly, the intelligence to self-program itself.

21 Unmanned aerial vehicle (UAV) Intelligent sensing network (SHM) Pattern perception Treatment & Drug development Intelligent Robotics Financial system Telecommunication network

22 Publications M. SHEN, C.-L. CHEN, K. KIM, B. CHO, A. TUDOR, AND Y. CHEN, ANALOG NEUROMORPHIC MODULE BASED ON CARBON NANOTUBE SYNAPSES, ACS NANO, (2013). B. CHO, K. KIM, C.-L. CHEN, A.M. SHEN, Q. TRUONG, AND Y. CHEN, NONVOLATILE ANALOG MEMORY TRANSISTOR BASED ON CARBON NANOTUBES AND C60 MOLECULES, SMALL, 1-5(2013). K. KIM, C.-L. CHEN, Q. TRUONG, A.M. SHEN, AND Y. CHEN, A CARBON NANOTUBE SYNAPSE WITH DYNAMIC LOGIC AND LEARNING, ADVANCED MATERIALS, , (2013). T. LEE, AND Y. CHEN, ORGANIC RESISTIVE NONVOLATILE MEMORY MATERIALS, MRSBULLETIN, 37(2) , (2012). C-L CHEN, K. KIM, Q.TRUONG, A SHEN, AND Y. CHEN, A SPIKING NEURON CIRCUIT BASED ON ACARBON NANOTUBE TRANSISTOR, NANOTECHNOLOGY, 23(27), 1-6, (2012). Y. S. AHN, K. KIM, H. K. PARK, H. T. HAHN, AND Y. CHEN, FUNCTIONALIZED CARBON NANOTUBE NETWORKS WITH FIELD-TUNABLE BANDGAPS, ADVANCED MATERIALS, 23, , (2011). Y. LEI, S. HUANG, P. SHARIF-KASHANI, Y. CHEN, P. KAVEHPOUR, $ T. SEGURA, INCORPORATION OF ACTIVE DNA/CATIONIC POLYMER POLYPLEXES INTO HYDROGEL SCAFFOLDS, BIOMATERIALS,,34, , C. STUART, H.-K. PARK, AND Y. CHEN, FABRICATION OF A 3D NANOSCALE CROSSBAR CIRCUIT BY NANOTRANSFER-PRINTING LITHOGRAPHY, SMALL, 6, (2010). L. ZHANG, Q. LAI, AND Y. CHEN, CONFIGURABLE NEURAL PHASE SHIFTER WITH SPIKE-TIMING-DEPENDENT PLASTICITY, IEEE ELECTRON DEVICE LETTERS, 31, (2010). Q. LAI, L. ZHANG, W.F. STICKLE, R.S. WILLIAMS, AND Y. CHEN, IONIC/ELECTRONIC HYBRID MATERIALS INTEGRATED IN A SYNAPTIC TRANSISTOR WITH SIGNAL PROCESSING AND LEARNING FUNCTIONS, ADVANCED MATERIALS, 22, 1-6 (2010).

23 Acknowledgement Group members AFOSR Collaborators: Mr. Andrew Tudor, M.S., Ph.D. Student Dr. Kyunghyun Kim, Ph.D. Postdoc Dr. Alex Shen, Ph.D. Postdoc Mr. Chris Shaffer, Ph.D student Mr. Cameron Danesh, Ph.D. student Dr. Yong Sik Ahn Dr. Chia-Ling Chen, Postdoc Dr. Byungjin Cho, Postdoc Dr. Junho Cheon, Postdoc Dr. Jongeun Ryu, Postdoc Mr. Dongwon Lee, M.S. Mr. Andy Truong, M.S. Mr. Sherwin Yee, M.S. Mr. Ryan Revilla, M.S. Mr. Yean Lee, Ph.D. student Stanford Prof. Fu-kuo Chang UBC Prof. Frank Ko UCLA Prof. Tom Hahn Prof. Chih-Ming Ho

24 Questions? Thanks!