small is the new big: data analytics on the...
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Small is the New Big: Data Analytics on the EdgeAn overview of processors and algorithms for deep learning techniques on the edge
Dr. Abhay Samant
VP Engineering, Hiller Measurements
Adjunct Faculty, University of Texas
Agenda
• Edge Computing
• Applications: Self-Driving Cars, Security in IMD, RF Machine Learning
• Processor Landscape: GPUs, FPGA
• Algorithms
Edge Computing
• Cloud Computing is going through a fundamental shift
• Centralized vs De-centralized architecture
• Edge Computing brings core building blocks• Compute
• Storage
• Network
Cloud Edge
• Extension of public cloud• CDNs are example of this
topology
• Cloud Edge HW is maintained by cloud provider
• Think of it as an extension to the public code
• Think from a business angle
Cloud Edge
Device Edge
• Device Edge:• Specialized device acting as node
gateway that mimics public cloud capabilities
• Customers own the hardware that runs the edge software stack
• AWS Green Grass and Microsoft Azure
• Bring device registry, device twins, communication, local storage and sync capability
Device Edge
Moore’s Law & Commercial Technology Impact
ADCs / DACs CPUs / FPGAs RF Components
Courtesy of ADMS Design AB Courtesy of Steve Cherry, IEEE Spectrum, July 2004Courtesy of Steven Pemberton
Courtesy: NI
ADAS Architectures Continue to Evolve
Sensor
Electronic Control Module (ECM)
Source: electronics-eetimes
SMART SENSORS/DECENTRALIZED PROCESSING RAW SENSOR DATA/CENTRALIZED PROCESSING
HYBRID SENSOR/PROCESSING
ADAS Sensor Fusion Example
RACAM(RADAR + CAMERA)
INTELLIGENT FORWARDVIEW CAMERA (IFV-100)
COLLISIONMITIGATION SYSTEM (CMS)
Deep Learning For Self-Driving Cars
• Environmental perception is key to autonomous driving, e.g. lane position
• Traditional feature recognition and image processing techniques don’t scale to needed complexity
• Deep neural networks learn efficient feature representation
• Inductive learning leads to evolving software operation that is challenging to test
DEEP NEURAL NETWORK
Machine Learning in RF Systems
• Some unique characteristics of RF ML• Data Rate is much higher
• RF signals are represented as complex numbers
• MIMO Systems
• Mixed signals (bits, complex-valued, RF)
• Protocol-based signals
Feature Learning
• Existing expertise used to best describe RF signals pertinent to a specific RF task.
• Deep Learning has achieved excellent performance in vision and speech applications by learning features similar to those learned by the brain from sensory data.
• Can machine learning of RF features help with many of the spectrum challenges?
Attention and Saliency
• Next-generation RF systems moving from MHz to GHz of spectrum.
• Requires focus on the right signals, ability to ignore others. Humans are exquisite at consuming, prioritizing, and processing visual and auditory information.
• Top-down attention is a goal-driven mechanism, which causes us to focus our cognitive processing on visual information most pertinent to a task at hand.
• Can be myopic, attention is complemented by a bottom-up (e.g. data-driven) mechanism called saliency
• For understanding an RF scene, stored RF concepts such as signals and transmitter types can be used to identify RF objects and model behavior.
Autonomous RF Sensor Configuration and Waveform Synthesis
• Sensor Configuration• Ability to adapt RF front end configuration
• Analog front end• Beam steering patterns• Bandwidth, frequency, power
• Mimics visual processing in human beings• Increases application such as self-driving cars
• Waveform Synthesis• Present systems allow to pick between two defined waveforms• ML techniques allow RF systems to synthesis a new waveform• How to share key parameters with receivers?
Security in Implantable Medical Devices
Data Storage SecurityNetwork and
Transmission SecurityApplication Layer
Security
Availability Efficiency
QualityReliability
Robustness Access
AuthorizationAuthentication
nVIDIA Jetson TX2
• Integrated SoC• 256-core NVIDIA Pascal GPU• Hex-core ARMv8 64-bit CPU complex• 8GB of LPDDR4 memory with a 128-bit
interface.
• The CPU complex combines a dual-core NVIDIA Denver 2 alongside a quad-core ARM Cortex-A57.
• Fits a small Size Weight, and Power (SWaP) footprint• 50 x 87 mm• 85 grams• 7.5 watts of typical energy usage.
• Jetson TX1 available for lower resources The Jetson TX2 module
nVIDIA Platform Architecture
16nm nVIDIATegra Parker
cuDNN and TensorFlow RT
libraries
Recurrent Neural NetLong Short Term
MemoryOnline reinforcement
Multimedia streaming network
2 Pascal Streaming multi-core processors
128 CUDA Cores
FPGA as viable platform for ML
• Currently, GPUs are considered good for ML algorithms such as DNN• Regular parallelism
• Optimized for TFLOPS
• New advances in FPGA and ML algorithms could change this trend
• Intel 14nm Stratix10 FPGA is one example
• Increased floating point DSPs
• On-chip RAMs• Improved
Frequenices• High BW Memories
• Exploiting sparsity in datasets
• Lower bit resolution
HW Trends
Algorithm Trends
Understanding ML Algorithms
Rosenblatt, Physiological Review, 1958, posed three questions1. How is information about physical world sensed or detected by biological system?2. In what form is information stored or remembered?3. How does information influence recognition and behavior?
Understanding ML Algorithms
• Basic perceptron operations used
• Across multiple ensembles and layers
• Same input applied for all weights
• Activation Function
• Bias setting at each level sets initial conditions
• Convolutional Neural Networks• Same set of weights used across
inputs
Testbed System Level Architecture• System View
• Resource Utilization
• Node Management
• Nodes• Simple filters
• Algorithms
• Neural nets
• Custom
• Network Graph• Topology of neural network
• Neural Network Sensitivity• Manages sensitivity of neural nets
• Neuron View• View of what the neuron sees (image, signal, ….)
System
Network GraphNeural Network
Sensitivity10-Best
Nodes
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