Mixed-signal neuromorphic VLSI devices for spiking neural network
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1 Mixed-signal neuromorphic VLSI devices for spiking neural network Ning Qiao Institute of Neuroinformatics University of Zurich and ETH Zurich Jun 20, 2018 ADAC6 Workshop Ning Qiao Neuromorphic CogniHve Systems INI UZH ETH Zürich 1
2 Outlines! Brain inspired computing! Neuromorphic engineering! Analog synapse and neuron circuits! Multi-core Neuromorphic architectures! Applications Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 2
3 The cost of current computing technologies is not sustainable l l l l In 2017 > 10 zettabytes of data were produced. IT infrastructures and consumer electronics absorbed > 10% of the global electricity supply. By 2025, over 50 billion of Internet-of- Things (IoT) devices will be interconnected. Over 180 zettabytes of data will be generated annually, potentially leading to a consumption of one-fifth of global electricity. Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 3
4 Current trends in computing HWs Ning Qiao Neuromorphic Cogni2ve Systems INI UZH ETH Zürich 4
5 Brain-Inspired compubng l Slow, noisy and variable processing elements l Massively parallel distributed computation, local connectivity l Real-time interaction with the environment 1mg weight 1mm 3 volume neurons 10e-15 J/spike <100 uw l Complex spatio-temporal pattern recognition l Foraging, navigation, language, and social behavior Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 5
6 Neuromorphic Compu?ng vs. Neuromorphic Engineering Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 6
7 Current trends in neuromorphic processors Not so radically different acer-all (not solving the von Neumann bogleneck problem) Ning Qiao Neuromorphic Cogni2ve Systems INI UZH ETH Zürich 7
8 Current trends in neuromorphic processors Not so radically different acer-all (not solving the von Neumann bogleneck problem) Ning Qiao Neuromorphic Cogni2ve Systems INI UZH ETH Zürich 8
9 Listen to the silicon (original approach) Mixed-signal analog/digital neuromorphic systems! Analog/digital computation, digital asynchronous communication.! Directly emulate the physics of neural systems.! Massively parallel collections of non-linear circuits.! Realistic neural and synaptic dynamics! Distributed memory! Co-localized memory and computation Ning Qiao Neuromorphic Cogni2ve Systems INI UZH ETH Zürich 9
10 Channel current-voltage rela3onships Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 10
11 Analog circuits Direct emula2on of synap2c dynamics [Bartolozzi and Indiveri, 2007] Ning Qiao Neuromorphic Cogni2ve Systems INI UZH ETH Zürich 11
12 Analog circuits Direct emulacon of neuron dynamics Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 12
13 Spike-based plas3city VLSI implementa3on Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 13
14 Cor3cal networks: a high degree of clustering Pyramidal Cell of Layer 3 of Cat Visual Cortex. Dendrites (Green), Axon (Red), Clusters of Boutons (Black) in Layer 3 and 5. Scale bar, 500 µm [R.J. Douglas and K.A.C. Martin, Neuron, 2007] [Moradi and Indiveri 2014] Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 14
15 Memory opamized mula-core neural architecture Hierarchical rouang with heterogeneous memory structures! Two-stage + 2D tree + 2D mesh multi-cast routing schemes using both source-address and destination-address encoding.! Fully asynchronous hierarchical routers for intra-core (R1), inter-core (R2) and inter-chip (R3) connectivity.! Embedded asynchronous CAM and SRAM cells distributed across and within cores. Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 15
16 Co-localized memory and computation FD-SOI design, ready for beyond CMOS technology Multiple parallel I/O pathways Multiple distributed asynchronous SRAM LUTs Distributed multi-bit TCAM cells Capacitors for state dynamics and learning Ideal for integration with (binary) resistive memories Ideal for integration with (learning) memristive devices Ideal for integration in 3D VLSI technology Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 16
17 Latest NP chip specs A large-scale, multi-core, neuromorphic processor DynapSEL in 28 nm FDSOI, is reported in ISSCC 2018 Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 17
18 Neural dynamics with appropriate 3me constants Paradigm shift Radically different from von Neumann architectures. Co-localized memory and computation. No virtual time (time represents itself). Data/event driven computation. Slow (biologically plausible) time constants For interacting with the environment in real-time. Inherently synchronized with the real-world natural events. To process natural sensory signals efficiently (low bandwidth/power). Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 18
19 Event-based convolutional network Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 19
20 Real-Cme autonomous behaving agents Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 20
21 Connecting neuromorphic processors to neuromorphic sensors and robots Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 21
22 Hardware preliminary (state-of-the-art) results INPUT SIGNAL SHAPE INPUT SIGNAL SOURCE NEURONS RESERVOIR RASTER PLOT RESERVOIR NEURONS OUT NEURONS PREDICTED ACTIVITY N 0 N 1 OUTPUT LAYER Ning Qiao Neuromorphic Cogni2ve Systems INI UZH ETH Zürich 22
23 Distributed ArDficial Intelligence Autonomous sensory-motor systems embedded systems & emerging memory technologies Brain Machine Interfaces & prosthetics Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 23
24 Team Work: InsCtute of NeuroinformaCcs Ning Qiao (INI) Yulia Sandamirskaya (INI) Lorenz Mu ller (INI) Melika Payvand (INI) Elisa Donati (INI) Dongchen Liang (INI) Raphaela Kreise (INI) Moritz Milde (INI) Marc Osswald (insightness) Dora Sumislawska (GeorgiaTech, USA) Fabio Stefanini (Columbia Univ., USA) Jonathan Binas (Univ. Montreal, CA) Emre Neftci (UC Irvine, USA) Saber Moradi (Yale, USA) Hesham Mostafa (UCSD, USA) Chiara Bartolozzi (IIT, Italy) Elisabetta Chicca (Univ. Bielefeld, DE) Stefano Fusi (Columbia Univ., USA) Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 24
25 Technology-transfer effort to commercialize Dr. Ning Qiao Prof. Giacomo Indiveri Dr. Kynan Eng Dr. Dylan Muir Dr. Sadique Sheik Dr. Qian Liu Felix Bauer Carsten Nielsen Ole Richter Anita Tuomi Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 25
26 The end Thank you for your attention Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 26
27 Spiking Neuron Network (SNN) Ning Qiao Neuromorphic Cogni3ve Systems INI UZH ETH Zürich 27
28 Spiking Neuron Network (SNN) Communication Computation Learning Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 28
29 Co-localized memory and computation FD-SOI design, ready for beyond CMOS technology Intel i7-4960x DYNAP-SEL No I/O bottleneck No memory bottleneck Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 29
30 A closed-loop bi-direceonal BMI Ning Qiao Neuromorphic Cognitive Systems INI UZH ETH Zürich 30
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