The age of mainframes is long over, the home PC is a common household device now, and cloud computing is slowly taking over the world. Well, Moore’s Law has taken over and now it is time for Edge AI: A confluence of Artificial Intelligence and edge computing, has emerged as a transformative phenomenon, enabling the processing of the data, analyzing it, and acting upon it in real-time.
Edge AI represents the amalgamation of two kinds of paradigms – the facilitation of execution of AI servers directly on the Edge device themselves or on local edge servers, rather than centralized or cloud-based systems.
The entire ecosystem of Edge AI brings with it a host of benefits, including reduced latency, enhanced privacy, improved reliability, and reduced bandwidth consumption.
EDGE AI Architecture – The Hardware
For a network architecture to be implemented on the EDGE, the obvious requirements are the components that comprise: EDGE-enabled devices, sophisticated architecture, and several network components. These can be classified into:
EDGE-capable ASICs – Application Specific Integrated circuits that embed the power of EDGE computing in them.
Field-Programmable Gate Arrays (FPGAs): Reconfigurable circuits that can be reprogrammed to accelerate various AI algorithms.
Neural Processing Units: Instead of CPUs or Industry-standard GPUs for AI, EDGE architectures need dedicated processors designed to accelerate neural computations, either on the EDGE device or on nearby AI processing units.
Tensor Processing Units – Tensor Processing Units (TPUS) are custom-developed ASICs designed exclusively for EDGE computing.
System-on-Chip (SoC) Solutions – Integrated circuits that Integrated circuits that combine CPU, GPU, NPU, and other specialized components on a single chip, optimized for EDGE deployment.
Containerization – These include containers that run in the cloud in a containerized manner, e.g. Docker and Kubernetes.
EDGE AI Architecture – The Software
Most EDGE AI computing requires software that is equally sophisticated and portable to operate (an imperative in EDGE computing). These include:
Lightweight AI Frameworks – Optimized versions of popular EDGE frameworks like TensorflowLite, ONNX Runtime, and PyTorch Mobile, software whose architecture is specially designed to run on hardware–constrained EDGE devices
Model Optimization Techniques – Quantization, pruning, and knowledge distillation software that deploy methodologies to reduce the overall volume of the data and its complexities for easier for computing at the device level.
Edge Orchestrated Platforms – These are holistic platforms that use distributed machine learning approaches to enable the training of models across distributed EDGE devices. These include platforms such as EdgeX and AWS IoT Greengrass contributing to both EDGE devices and AI workload distribution.
Federated Learning Frameworks – Simply put, Federated Learning Frameworks is a novel approach to distributed learning systems in Machine Learning that facilitates model training across decentralized EDGE devices while maintaining data privacy at the same time.
Edge Native Operating System – It’s a no-brainer that someone would have invented an operating system in EDGE devices. The genesis of this development can be traced back to the early days of IoT, such as Raspberry Pi and Arduino, one of the earliest EDGE devices and the precursor to IIoT, relying on several flavors of Linux or sometimes, their own Oss.
The Final Protocol – EDGE Network Architecture
Where a process is intelligently running on its hardware and software, it is only natural that the network infrastructure must be robust, to say the least. EDGE AI relies on just such protocols to function effectively:
5G Networks- The advent of 5G brings with it ultra-low latency and high bandwidth networks capable of real-time data processing and model updating.
TSN – Standing for Time Sensitive Networking, these are Ethernet-based protocols, you guessed it, reduce latency and compute time. This is critical for sensitive EDGE Applications in industries like Healthcare and BFSI
Low-Power Wide Area Networks – Emerging technologies in EDGE computing, such as LPWAN, RoRaWan, and Narrow Band IoT guarantee quantified latency for critical EDGE Applications.
LoPoWAN – Low Power Wide Area Networks ensure that EDGE devices are constantly functioning and communicating with each other, consuming the lowest power possible to keep devices running constantly, especially in areas of low power infrastructure.
Mesh Networking – Networking Topologies that are decentralized empower EDGE devices to communicate and share AI insights without relying on centralized AI infrastructure.
DID WE SAY FINAL LAYER? HOLD ON! ONE MORE SET OF LAYERS PLEASE
There are no insights derived from EDGE devices (yes, they are still that expensive), without talking about the Cognitive Layer of Edge AI. This is where all the magic happens. The Cognitive Layer of Edge Computing is, perhaps, the most important layer in a typical EDGE AI architecture:
Sensory Layer – This layer comprises sensors and all input devices that provide the data for EDGE computing. They capture data from the environment from devices such as sensors and computers.
Preprocessing Layer – Unlike ML, EDGE AI also needs lightweight algorithms for the normalization and cleansing of data, and feature extraction for further processing of the insights thus gathered.
Inference Layer – This layer implements the AI model to perform tasks such as speech detection or anomaly detection on the preprocessed data.
Decision Layer – The decision layer outputs the decision made by the multiple inference models and applies heuristics to make actionable decisions.
Actuation Layer - In many ways, this is the crux of EDGE AI computing. Using Intelligent actionable insights to put into action via physical or digital outputs, such as controlling robotic systems or triggering alerts.
Learning Layer – A unique functionality of any EDGE, or any AI system, is the ability to learn from its past performance, and EDGE computing is no different. Every EDGE device has at least one chip to continuously improve and iterate upon its performance.
In conclusion, Edge AI represents a seismic shift in the AI landscape, promising real-time processing, reduced latency, and enhanced security. As this technology continues to evolve, we can expect transformative innovations across industries, redefining the frontiers of artificial intelligence.
WHERE IS THE TALENT GAP FALLING SHORT?
For the sake of brevity, we’ll name the skillsets that are most in demand in EDGE AI right now and the functions in which this technology is facing a massive talent gap (2024):
Embedded Systems Engineering: Proficiency in designing and optimizing software for resource-constrained devices.
Machine Learning Optimization: Expertise in model compression, quantization, and efficient inference techniques.
Distributed Systems: Understanding of federated learning, edge-cloud collaboration, and decentralized architectures.
Hardware-Software Co-design: Ability to optimize AI algorithms for specific hardware accelerators and edge platforms.
Real-time Systems: Knowledge of deterministic computing, time-sensitive networking, and low-latency processing.
Security and Privacy: Familiarity with encryption, secure enclaves, and privacy-preserving machine learning techniques.
Domain-specific Knowledge: Understanding of the specific requirements and constraints of industries adopting Edge AI.
Conclusion
The urgent need for EDGE AI specialists presents both a challenge and opportunities for the tech workforce. As we stand at the dawn of this new era of distributed intelligence, the onus falls upon us to cultivate the expertise necessary to navigate its complexities and harness its transformative power for the betterment of society.
