Small is the New Big: Data Analytics on the Edge

Transcription

1 Small is the New Big: Data Analytics on the Edge An overview of processors and algorithms for deep learning techniques on the edge Dr. Abhay Samant VP Engineering, Hiller Measurements Adjunct Faculty, University of Texas

2 Agenda Edge Computing Applications: Self-Driving Cars, Security in IMD, RF Machine Learning Processor Landscape: GPUs, FPGA Algorithms

3 Edge Computing Cloud Computing is going through a fundamental shift Centralized vs De-centralized architecture Edge Computing brings core building blocks Compute Storage Network

4 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

5 Courtesy: NI 5G for IoT Applications

6 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

7 Moore s Law & Commercial Technology Impact ADCs / DACs CPUs / FPGAs RF Components Courtesy of ADMS Design AB Courtesy of Steven Pemberton Courtesy of Steve Cherry, IEEE Spectrum, July 2004 Courtesy: NI

8 Machine Learning Application Landscape

9 The Connected Car V2V V2V

10 Sensor Fusion ULTRASONIC LIDAR CAMERA SHORT RANGE RADAR LONG RANGE RADAR

11 ADAS Architectures Continue to Evolve SMART SENSORS/DECENTRALIZED PROCESSING RAW SENSOR DATA/CENTRALIZED PROCESSING Sensor Electronic Control Module (ECM) HYBRID SENSOR/PROCESSING Source: electronics-eetimes

12 ADAS Sensor Fusion Example COLLISION MITIGATION SYSTEM (CMS) RACAM (RADAR + CAMERA) INTELLIGENT FORWARD VIEW CAMERA (IFV-100)

13 Deep Learning For Self-Driving Cars DEEP NEURAL NETWORK 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

14 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, complexvalued, RF) Protocol-based signals

15 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?

16 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.

17 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?

18 Security in Implantable Medical Devices Availability Efficiency Robustness Access Data Storage Security Network and Transmission Security Application Layer Security Reliability Quality Authentication Authorization

19 Machine Learning Compute Platforms

20 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

21 nvidia Platform Architecture 16nm nvidia Tegra Parker Multimedia streaming network cudnn and TensorFlow RT libraries 2 Pascal Streaming multi-core processors Recurrent Neural Net Long Short Term Memory Online reinforcement 128 CUDA Cores

22 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

23 Understanding ML Algorithms Rosenblatt, Physiological Review, 1958, posed three questions 1. 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?

24 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

25 Testbed System Level Architecture System View Resource Utilization Node Management System Nodes Simple filters Algorithms Neural nets Custom Nodes Network Graph Topology of neural network Neural Network Sensitivity Manages sensitivity of neural nets Neuron View View of what the neuron sees (image, signal,.) Network Graph Neural Network Sensitivity 10-Best

26 System Level Architecture Three key blocks Training Inferencing -- Analysis

27 Summary Edge Computing Applications: Self-Driving Cars, Security in IMD, RF Machine Learning Processor Landscape: GPUs, FPGA Algorithms