Brainchip 1 - hotcopper.com.au
Transcript of Brainchip 1 - hotcopper.com.au
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Agenda
Neuromorphic computing backgroundAkida Neuromorphic System-on-Chip (NSoC)
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Neuromorphic Computing Background
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A Brief History of Neuromorphic Computing
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Semiconductor Compute Architecture Cycles
Disruption
Consolidation
CPU/MPU/GPU
Architectural Von Neumann Harvard
Multiplicity of ISAs Multiplicity of Vendors Multiplicity of accelerators
FPU GPU DSP
AlexNet winsImagenet Challenge
X86/RISCGPUFPGA
1971Intel 4004Introduced
Artificial Intelligence Acceleration
2012
Acceleration Convolutions Spiking
Architecture VLIW Array Memory
Datatype Floating Fixed Binary1990
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The Next Major Semiconductor Disruption
Source: Tractica Deep Learning Chipsets, Q2 2018
$60B opportunity in next decade
Training is important, but inference is the major market
Machine learning requires dedicated acceleration 0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
2018 2019 2020 2021 2022 2023 2024 2025
$M
AI Acceleration Chipset Forecast
Training
Inference
General Purpose
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Explosion of AI Acceleration
Software Simulation of ANNs X86 CPU
Convolutional Neural Networks
Neuromorphic Computing
TrueNorth Test Chip
Customized Acceleration
Edge Acceleration
Re-Purposed Hardware Acceleration
LoihiTest Chip
Google TPU
Cloud Acceleration
X86 CPU
+ Internal ASIC Development
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Memory
Control unit
PROCESSOR
Arithmetic logic unit
input
output
ACCUMULATOR
Traditional CPU Architecture Inefficient for ANNs
Optimal for sequential execution Distributed, parallel, feed-forward
Traditional Compute Architecture Artificial Neural Network Architecture
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ANN Differences – Primary Compute Function
Convolutional Neural NetworkSpiking Neural Network
Inhibited connections
Reinforced connections
∫
Synapses
Neurons
Spikes
∫
Linear Algebra Matrix Multiplication
∫ ∫
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Neural Network Comparison
Convolutional Neural Networks Spiking Neural Networks
Characteristic Result Characteristic Result
Computational functions
Matrix Multiplication, ReLU, Pooling, FC layers
Math intensive, high power, custom acceleration blocks
Threshold logic, connection reinforcement
Math-light, low power, standard logic
Training Backpropagation off-chip
Requires large pre-labeled datasets, long and expensive training periods
Feed-Forward, on or off-chip
Short training cycles, continuous learning
Math intensive cloud compute Low power edge deployments
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Previous Neuromorphic Computing Programs
Primarily research programsInvestigating neuron simulation
1,000’s of ways to emulate spiking neuronsInvestigating training methods
Academia or government programsSpiNNaker (Human Brain Project)IBM TrueNorth (DARPA)Neurogrid (Stanford)Intel Loihi test chip
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Culmination of Decades of Development
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World’s first Neuromorphic System on Chip (NSoC)Efficient neuron modelInnovative training methodologies
Everything required for embedded/edge applicationsOn-chip processorData->spike conversion
Scalable for Server/CloudNeuromorphic computing for multiple markets
Vision systemsCyber securityFinancial tech
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Akida NSoC Architecture
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Akida Neuron Fabric
Most efficient spiking neural network implementation
1.2M Neurons10B Synapses
Able to replicate most CNN functionality
ConvolutionPoolingFully connected
Right-Sized for embedded applications10 classifiers (CIFAR 10)
11 Layers517K Neurons616M Synapses
Meets demanding performance criteria1,100 fps CIFAR-1082% accuracy
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Neuron and Synapse Counts in the Animal Kingdom
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The Most Efficient Neuromorphic Computing Fabric
Relative Implementation Efficiency(Neurons and Synapses)
300X
3X
Fixed neuron modelRight-sized Synapses minimized on-chip RAM
6MB compared to 30-50MBProgrammable training and firing thresholds
Flexible neural processor coresHighly optimized to perform convolutionsAlso fully connected, pooling
Efficient connectivityGlobal spike bus connects all neural processorsMulti-chip expandable to 1.2 Billion neurons
Keys to efficiency
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Neuromorphic Computing Benefits
Frames per Second/watt
Top-
1 Ac
cura
cy
GoogLeNetIntel
Myriad 2
4.2 fps/w
69% ~$10
Cifar-10 Intel
Myriad 2 79%
18 fps/w
~$10
Cifar-10 BrainChip
Akida
1.4K fps/w
82% ~$10
Cifar-10 IBM TrueNorth
83%
6K fps/w
~$1,000
Cifar-10 Xilinx ZC709
80%
6K fps/w
~$1,000
GoogLeNetTegra TX2
69%
15 fps/w
~$300
Tremendous throughput with low power
Math-lite, no MACsNo DRAM access for weights
Comparable accuracyOptimized synapses and neurons ensures precision
Note: For comparison purposes only. Data and pricing are estimated and subject to change
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Akida NSoC Applications
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Vision Applications: Object Classification
Lidar
Pixel
DVS
Ultrasound
Data InterfacesNeuron Fabric
Metadata
MetadataMetadata
Metadata
Sens
or In
terf
aces
Conv
ersio
n Co
mpl
ex
010101100101011001010110
SNN ModelObject Classification
Data
Inte
rfac
es
Complete embedded solutionFlexible for multiple data types
<1 WattOn-chip training available for continuous learning
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Financial Technology Applications: Fintech Data Analysis
Fintech Data
Neuron FabricMetadata
MetadataMetadata
Metadata
Conv
ersio
n Co
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010101100101011001010110
SNN ModelPattern Recognition
Data
Inte
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Unsupervised learning on chip to detect repeating patterns (Clustering)These trading patterns and clusters can then be analyzed for effectiveness
CPU
01010110
Fintech data – distinguishing parameters for stock characteristics and trading information, can be converted to spikes in SW on CPU or by Akida NSoC
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Cybersecurity Applications: Malware Detection
File or packet properties
Neuron FabricMetadata
MetadataMetadata
Metadata
Conv
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n Co
mpl
ex
010101100101011001010110
SNN ModelFile Classification
Data
Inte
rfac
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Supervised learning for file classification based on file properties
CPU
01010110
File or packet properties – distinguishing parameters for files/network traffic, can be converted to spikes in SW on CPU or by Akida NSoC
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Cybersecurity Applications: Anomaly Detection
Behavior Properties
Neuron FabricMetadata
MetadataMetadata
Metadata
Conv
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mpl
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010101100101011001010110
SNN ModelBehavior classifiers
Data
Inte
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Supervised learning on known good behavior and anomalous behavior
CPU
01010110
Behavior properties can be CPU loads for common applications, network packets, power consumption, fan speed, etc..
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Creating SNNs: The Akida Development Environment
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AKIDA Training Methods
Unsupervised learning from unlabeled dataDetection of unknown patterns in dataOn-chip or off-chip
Unsupervised learning with label classificationFirst layers learns unlabeled features, labeled in fully connected layerOn-chip or off-chip
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World’s first NSoCLow power and footprint of neuromorphic computingHighest performance /w/$Estimated tape-out 1H2019, samples 2H2019
Complete solution for embedded/edge applications – but scalable for cloud/server usage