Photonic Neuromorphic computing

Transcription

1 Photonic Neuromorphic computing Presenter: Rafatul Faria PhD student, ECE, Purdue University Major: Micro and Nanoelectronics (MN)

2 Outline General Overview of Neuromorphic computing What is neuromorphic computing and why is it important? Basics of neurons and synapses, learning Different technologies targeting neuromorphic computing Photonic Devices for Neuromorphic computing Different photonic devices for neuromorphic computing Pros and cons Future directions

3 Outline General Overview of Neuromorphic computing What is neuromorphic computing and why is it important? Basics of neurons and synapses, learning Different technologies targeting neuromorphic computing Photonic Devices for Neuromorphic computing Different photonic devices for neuromorphic computing Pros and cons Future directions

4 What is neuromorphic computing? Neuromorphic computing Mimicking human brain function for low energy, high speed cognitive computing and learning human brain contains over 100 billion neurons and 100 trillion to 150 trillion synapses. Power consumption: roughly 20 Watts!!! smart phones, sensor networks, selfdriving automobiles, robots, public safety, medical imaging, real-time video analysis, signal processing, olfactory detection, and digital pathology and so on

5 Why is neuromorphic computing important? GPU vendors Nvidia, AMD IBM truenorth (2014) (DARPA funded custom hardware) Google: Tensor Processing unit (TPU) Apple Building an efficient neuromorphic chip Facebook Mircosoft Intel Loihi (2017) one million programmable neurons and 256 million synapses

6 Why is neuromorphic computing important? GPU vendors Nvidia, AMD IBM truenorth (2014) (DARPA funded custom hardware) Google: Tensor Processing unit (TPU) Apple Building an efficient neuromorphic chip Facebook Mircosoft Google DeepMind AI program AlphaGo (March 2016) Intel Loihi (2017) one million programmable neurons and 256 million synapses

7 Biological neuron Neural Network: neurons and synapses Diep et al., APL, 2014 Modeling a simple Perceptron neuron Mathematical operations: Multiplication summation

8 Biological neuron Neural Network: neurons and synapses Diep et al., APL, 2014 Modeling a simple Perceptron neuron Mathematical operations: Multiplication summation

9 Biological neuron Neural Network: neurons and synapses Diep et al., APL, 2014 Modeling a simple Perceptron neuron Mathematical operations: Multiplication summation Artificial neural network (ANN)

10 Neural Network: neurons and synapses Stochastic spiking neuron Membrane potential Biological neurons are stochastic in nature Burkitt, Anthony N. "A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input." Biological cybernetics 95.1 (2006): 1-19.

11 Training a Neural Network Most widely used learning mechanism: Back propagation Nature, vol. 323, 1986

12 Moores law ending Beyond CMOS devices to mimic neuron

13 Moores law ending Beyond CMOS devices to mimic neuron CMOS based implementation of neural network: Von neuman bottleneck Low bandwidth between CPU and memory Majority power loss in data transfer process

14 Moores law ending Beyond CMOS devices to mimic neuron Neuromorphic computing CMOS based implementation of neural network: Von neuman bottleneck Low bandwidth between CPU and memory Majority power loss in data transfer process 2682 references!!!

15 Outline General Overview of Neuromorphic computing What is neuromorphic computing and why is it important? Basics of neurons and synapses, learning Different technologies targeting neuromorphic computing Photonic Devices for Neuromorphic computing Different photonic devices for neuromorphic computing Pros and cons Future directions

16 Why Photonic neuromorphic computing? Lecture 8 ECE 695 Nanophotonics and Metamaterials

17 Why Photonic neuromorphic computing? Lecture 8 ECE 695 Nanophotonics and Metamaterials Required properties of devices for neumorphic computing: High connectivity for parallel operation Low power Faster computing Collocating memory and processing Lower footprint area, scalable Photonic devices can potentially meet all these criteria

18 Schematic and operation of a LIF neuron Photonic Spiking neuron

19 Photonic Spiking neuron Schematic and operation of a LIF neuron Spiking neuron properties

20 Photonic Spiking neuron Schematic and operation of a LIF neuron Spiking neuron properties Potential photonic elements for fabricating LIF neuron

21 Spiking neuron (continued) First bench-top model for photonic neuron (LIF neuron) Carrier modulation nonlinear optical loop mirror (NOLM) G: Variable attenuator T: Tunable delay line : low power pulse train 1 SOA: Semiconductor Optical Amplifier

22 Spiking neuron (continued) First bench-top model for photonic neuron (LIF neuron) Carrier modulation nonlinear optical loop mirror (NOLM) G: Variable attenuator T: Tunable delay line : low power pulse train 1 SOA: Semiconductor Optical Amplifier Drawbacks: Performs integration and thresholding (2 required properties out of 5). Lacks reset condition, ability to generate pulses and truly asynchronous behavior. Fiber based neuron, larger footprint area, not scalable

23 Spiking neuron (continued) Simple application using previous bench-top fiber based neuron model Barn Owl Auditory Localization

24 Spiking neuron (continued) Simple application using previous bench-top fiber based neuron model Barn Owl Auditory Localization Input signals far apart: NO output spike Input signals close: output spikes

25 Excitable Laser Neuron Generalized model Rate equations: Gt ( ) : Gain Qt ( ) : Absorption It ( ) : Laser intensity A: Bias current of the gain B : Level of absorption a : differential absorption relative to differential gain G : Relaxation rate of gain : Relaxation rate of absorber Q : inverse photon lifetime I f( G) : small contribution to intensity due to noise

26 Excitable Laser Neuron Generalized model Threshold condition: G( t) Q( t) 1 Rate equations: Gt ( ) : Gain Qt ( ) : Absorption It ( ) : Laser intensity A: Bias current of the gain B : Level of absorption a : differential absorption relative to differential gain G : Relaxation rate of gain : Relaxation rate of absorber Q : inverse photon lifetime I f( G) : small contribution to intensity due to noise Smaller footprint area, scalable

27 VCSEL Neuron VCSEL: Vertical Cavity Surface Emitting Laser

28 VCSEL Neuron VCSEL: Vertical Cavity Surface Emitting Laser Scalable Low power

29 Silicon photonic weight bank MRR: Microring resonator BPD: Balanced photo diode LD: Laser diode MZM: Mach-Zehnder modulator (neuron) WDM: Wavelength-division-multiplexer AWG: Arrayed waveguide grating Neuron 1 Microring resonator as weight bank Experimental set up Neuron Neuron 4 Tait, Alexander N., et al. "Neuromorphic photonic networks using silicon photonic weight banks." Scientific Reports 7.1 (2017): 7430.

30 Silicon photonic weight bank Weight logic implemented by tunable microring resonator (MRR) Hybrid approach: optical+electrical MRR: Microring resonator BPD: Balanced photo diode LD: Laser diode MZM: Mach-Zehnder modulator (neuron) WDM: Wavelength-division-multiplexer AWG: Arrayed waveguide grating Neuron 1 Microring resonator as weight bank Experimental set up Neuron Neuron 4 Tait, Alexander N., et al. "Neuromorphic photonic networks using silicon photonic weight banks." Scientific Reports 7.1 (2017): 7430.

31 Fully optical neural network Fully optical neural network (ONN) ONN composed of Optical Interference unit (OIU) and Optical nonlinearity unit (ONU). OIU implements any real-valued matrix multiplication by using optical beam-splitters, phase shifters and attenuators. ONU can be implemented using common optical non-linearity such as saturable absorption e.g. Grphene saturable absorber. This scheme is experimentally demonstrated within a subset of a programable nanophotonic processor (PNP)- a silicon photonic integrated circuit fabricated in the OPSIS foundry. Fully optical Shen, Yichen, et al. "Deep learning with coherent nanophotonic circuits." Nature Photonics (2017)

32 Fully optical neural network (continued) Correlation matrices Two layer neural network for vowel recognition

33 2000 node coherent Ising machine with all-to-all connections All to all connection by FPGA module Max-Cut problem Inagaki, Takahiro, et al. "A coherent Ising machine for 2000-node optimization problems." Science (2016):

34 Summary A very blooming field which is already shaping our daily life. Many different areas are trying to come up with the best implementation using many different physics. Photonic application is very promising for low power high speed computing and transfer of data and may eventually be the winner. Ultimate goal: low power, high speed brain like computing Neuromorphic computing electronics spintronics Photonics/plasmonics

35 Thank you