Opportunities for ML Analytics at the Sensor Endpoint

Transcription

1 Opportunities for ML Analytics at the Sensor Endpoint Chris Rogers, CEO SensiML Corporation MAKING SENSOR DATA SENSIBLE

2 IoT Smart Devices How Many Qualify as Truly Smart? The Majority of IoT Endpoint Devices Incorporate sensors Connected but dumb Defer analytics elsewhere Network constrained Not real-time Stream unfiltered sensitive data Static algorithms What s missing is useful adaptable algorithms embedded in the device

3 So What s the Big Deal With Having Dumb IoT Sensors?

4 Conventional Dumb Sensor IoT Network Simple Sensor - Temperature Sensors - Limit Switches - Counters Key Challenges: Bandwidth Power Latency Complex Sensor - Cameras, imaging sensors - Audio, microphones - Motion, accelerometers, IMUs - Vibration, piezo sensors - Passive IR - Current, voltage, electrodes - RF signals Security

5 Conventional IoT Sensor Network: Bandwidth / Power Raw Payload Motion Vibration Audio Video Sample Rate 1 khz 5 khz 20 khz 30 Hz Resolution 16 bit 16 bit 16 bit 24 bit Channels 9 (x,y,z) 3 (x,y,z) 2 (stereo) 4 MPixel Throughput Req d 140 kbps 234 kbps 625 kbps 2.9 Gbps Network Throughput LPWAN (LoRA) LTE IoT (Cat-M1 R13) ZigBee BLE 4.2 (BT Smart) BT 5.0 WLAN (802.11ac) Payload < 8 kbps < 375 kbps < 250 kbps <21 kbps * < 1.4 Mbps < 125 kbps ** 200Mbps *** * BLE ms CI, 20 byte MTU ** BT5 2x long range operation *** 40Mhz channel, 1x1 (embedded STA)

6 Conventional IoT Sensor Network: Latency "In cases where sensors generate a lot of telemetry, but only sporadic data that's actionable, you want to discern the signal from the noise without overwhelming the ingestion processes at the core you don't want a 100 millisecond loop to the internet and back." - Jeffrey Hammond, Forrester Research Analyst

7 Conventional IoT Sensor Network: Security ALL Audio Local Wake Word Event Detection Alexa Specific Query Amazon AVS Server ALL Audio Amazon AVS Server

8 The Role for Sensors in Intelligent IoT Networks

9 Our Own Brains: A Distributed Processing Architecture

10 As Applied to IoT Analytics Processing A High-Performance Distributed IoT Network Application Cloud Analytics (offline data mining, business intelligence) Cloud NLP (speech recognition, automated assistant) Endpoint Rich Sensing ML (audio processing, motion and vibration classification) Local Critical Insight ML (network failure independent, mission critical feedback/control) Edge ANN/CNN (vision, spatial and image processing) Endpoint Real-Time ML (machine control, robotics)

11 Smart Sensors: Combine Rich Signals with Expert Insight Conventional Sensor Physical World Sensed Property Acquired Signal Signal Conditioning Communication Machine vibration Filtering, down-sampling, averaging, etc. Packetization, data compression, error correction Event Detection Expert Training Feature Engineering Classification Meaningful Insight Smart Sensor Extruder #3 Event Excess Vibration OK Cause Flange Bearing Fail Obstruction to provide local inferencing of meaningful events

12 A Distributed Smart Sensor Endpoint IoT Network Key Challenges: Processing Learning Data Loss

13 Smart Sensing: Processing Limitations

14 Algorithm Suitability to IoT Endpoint Device Processors Hand-coded Rules Based Machine Learning AI / Deep Learning Classic numerical methods (i.e. regression, heuristics, sorting, linear programming) Expert systems (Knowledge base collection of rules) Classification (i.e. SVM, knn, random forest, clustering) ANN / CNN (Neuron arrays trained by backpropagation) Efficient Execution Code/Rules Separation Efficient Execution Overall Performance Costly Development Brittle Logic Adaptive Learning HW Requirements Inflexible / Static Inefficient code Training Intervention Large Training Datasets

15 Smart Sensing: On-Device Learning Challenge Dilemma: If sensor nodes see only their own input data they can learn only from what they are exposed Cloud capable of seeing data from ALL sensors but then training is centralized not distributed

16 Smart Sensing: A Flexible Learning Architecture Level 1: Algorithm Tuning and Personalization Local model reconfiguration and parameter tuning Improved classifier performance over time No cloud required Example: Tailoring generic model to a specific user or device Level 2: Neuron Remapping Same event triggers and features, new classifier configuration Cloud invoked for redefinition of classifier stage (harnessing training data from all available sensors) On-the-fly model change initiated by cloud Example: Learn a new gesture or activity Level 3: Algorithm Reprogramming All new event triggers, features, and classifier Full algorithm reconstruction via the cloud (harnessing training data from all available sensors) Over-the-air sensor firmware update Example: Provide an entirely new application

17 Smart Sensing: Data Retention Limitations Throw away this stuff!?!? I never know when I might find a use for it! A Cloud Centric Big Data Analyst

18 Mitigations to the Need for Data Hoarding Classifier Driven Sampling Capture and store sampled anomalous raw data Parameterized Sampling Reduction of raw data to feature vectors Localized Model Personalization Per device customization of algorithm parameters (e.g. classifier weight factors)

19 The Role of Sensor Endpoints in IoT Analytics Processing Cloud Analytics (offline data mining, business intelligence) Cloud: Broad Contextual Insight Cloud NLP (speech recognition, automated assistant) Endpoint Rich Sensing ML (audio processing, motion and vibration classification) Local Critical Insight ML (network failure independent, mission critical feedback/control) Edge ANN/CNN (vision, spatial and image processing) Endpoint Real-Time ML (machine control, robotics) Endpoint: Localized Sensory Insight

20 Q & A