Accelerating intelligence at the edge for embedded and IoT applications
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1 Accelerating intelligence at the edge for embedded and IoT applications Arm Tech Symposia 2017
2 Agenda The emergence of intelligence at the edge Requirements for intelligent edge computing IP and technologies needed to succeed 2
3 Deployment of IoT systems is increasing 20.4 Billion *Source: Internet of Things - Endpoints and Associated Services, Worldwide, 2016 **Source: Data Age 2025, IDC
4 Turning large amounts of data into actionable insights Training / Learning Inference (e.g. surveillance cameras) Action 4 Cloud computing Challenges faced by cloud computing Real time / latency Autonomy Cloud Bandwidth computing / Cost Security Privacy
5 Turning large amounts of data into actionable insights Training / Learning Inference (e.g. surveillance cameras) Action 5 Cloud computing Cloud computing Challenges faced by cloud computing Real time / latency Autonomy Bandwidth / Cost Security Privacy
6 Edge computing Elements of cloud computing Data centers On premise Elements of edge computing Fog Network Gateways Routers >75% of wireless device installed base require gateways to connect Source: 2017 IHS data - Industrial IoT Segment By 2020, 90% of Internet of Things (IoT) projects will utilize an IoT gateway Gartner, Explore the Roles of IoT Gateways in Five Edge Use Cases, 16 June 2017 Actuators / Control Devices Sensors 6
7 Requirements for intelligent edge computing
8 Requirements for intelligent edge computing Secure Real time Bandwidth Privacy Cloud Sensor Actuator Local Smart Gateway Global Base Station Server Requirements Low power Cognitive processing Scalable Security Device management Cloud 8
9 Requirements for intelligent edge computing (2) Low power Cognitive processing Scalable Security Device management Cloud Energy efficient, always-on, fanless gateway device Machine learning (ML) and Artificial Intelligence (AI) Performance, bandwidth and connected devices Common security foundation, global standardisation Device provisioning, management, firmware updates Local storage, authentication, integrity 9
10 IP and technologies to succeed at the edge
11 Arm DynamIQ technology Rearchitecting the compute experience Designed from the ground up for AI Closely-coupled accelerators More intelligent systems Low power Cognitive processing Scalable Security Device management Cloud 11
12 Indicative Power Highly scalable, low power, 64-bit computing Higher processing Storage Networking Processing Networking Plugged in Unplugged Low power Cognitive processing Scalable Security Device management Cloud 12
13 Relative Performance NEON with embedded Machine Learning capabilities Relative Machine Learning performance Series1 Series2 Low power Cognitive processing Scalable Security Device management Cloud 13 Graphs show relative performance of hand-optimized SGEMM code
14 Closely-coupled accelerators and sensors Reads and writes into the CPU cache memory Enhanced capabilities for low latency, closely coupled accelerators Critical data stays on-chip, always secure DynamIQ cluster PP ACP (1) Configure registers for task (2) Fetches data from CPU memory (4) Writes result into CPU memory Accelerator (3) Carries out acceleration Low power Cognitive processing Scalable Security Device management Cloud 14
15 Single architecture, scalable from one end to the next Sensor Gateway Networking Data Center Single core Up to Eight (8) CPUs Up to 256 CPUs Up to 1000s of CPUs Low power Cognitive processing Scalable Security Device management Cloud 15
16 API gateways TrustZone security foundation for Cortex-A processors Trusted environment (secure) Flash programming Secure storage (certificates) Normal applications (Non-secure) App Middleware Authentication & provisioning Secure boot Secure storage (certificates) Health check App App App RTOS Hardware Low power Cognitive processing Scalable Security Device management Cloud 16
17 Gateway management with Mbed Cloud Edge Mbed Cloud Gateway device type in cloud device registration Mbed Cloud Edge core Gateway to proxy for protocol Gateway endpoint device Legacy protocol translator & control Manage and controls legacy devices connected Firmware update Update Mbed Cloud firmware Mbed Cloud Edge core Sensors Mbed Cloud Mbed Cloud Edge software components Legacy protocol translator & control Firmware update Low power Cognitive processing Scalable Security Device management Cloud 17
18 Enabling the growth of intelligence at the edge Demand for intelligent computing is naturally moving towards the edge Requires highly efficient, secure, scalable, intelligent computing that is inexpensive Arm provides a full breadth of IP and technologies to meet these requirements 18
19 Thank You! Danke! Merci! 谢谢! ありがとう! Gracias! Kiitos! 19
20 The Arm trademarks featured in this presentation are registered trademarks or trademarks of Arm Limited (or its subsidiaries) in the US and/or elsewhere. All rights reserved. All other marks featured may be trademarks of their respective owners. 20
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