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Brainchip OCTOBER 2017 | 1


Agenda

Neuromorphic computing backgroundAkida Neuromorphic System-on-Chip (NSoC)


Neuromorphic Computing Background


A Brief History of Neuromorphic Computing


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


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


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


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


ANN Differences – Primary Compute Function

Convolutional Neural NetworkSpiking Neural Network

Inhibited connections

Reinforced connections

∫

Synapses

Neurons

Spikes

∫

Linear Algebra Matrix Multiplication

∫ ∫


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


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


Culmination of Decades of Development


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


Akida NSoC Architecture


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


Neuron and Synapse Counts in the Animal Kingdom


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


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


Akida NSoC Applications


Vision Applications: Object Classification

Lidar

Pixel

DVS

Ultrasound

Data InterfacesNeuron Fabric

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aces

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SNN ModelObject Classification

Data

Inte

rfac

es

Complete embedded solutionFlexible for multiple data types

<1 WattOn-chip training available for continuous learning


Financial Technology Applications: Fintech Data Analysis

Fintech Data

Neuron FabricMetadata

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SNN ModelPattern Recognition

Data

Inte

rfac

es

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


Cybersecurity Applications: Malware Detection

File or packet properties


MetadataMetadata

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Conv

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n Co

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SNN ModelFile Classification

Data

Inte

rfac

es

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


Cybersecurity Applications: Anomaly Detection

Behavior Properties


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Conv

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n Co

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SNN ModelBehavior classifiers

Data

Inte

rfac

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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..


Creating SNNs: The Akida Development Environment


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


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

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