CHAPTER 1
Edge Foundation Models
The trajectory of artificial intelligence in 2026 is being shaped not only by the continued expansion of parameter counts that has defined the early 2020s, but also by a sustained push toward densification, efficiency, and architectural specialization. While cloud-based La ...
CHAPTER 2
Multimodal Edge Models
The definition of edge intelligence has expanded from processing single data streams, such as text or static images, to the coordinated synthesis of multiple sensory inputs. Multimodal Edge AI refers to systems that integrate vision, audio, radar, LiDAR, and inertial data ...
CHAPTER 3
Ultra-Low-Power Architectures for Edge Intelligence
As edge intelligence becomes more pervasive, the need for continuous, low-power intelligence has pushed hardware beyond the limits of the traditional von Neumann model. Rather than a bottleneck that can be managed through scaling, data movement has now become the dominant ...
CHAPTER 4
Agentic AI at the Edge
Over the past two years, interest in agentic AI systems has accelerated as large language models have made it possible to build software agents that plan, reason, and act autonomously toward defined goals. Most of these systems are currently deployed in cloud environments, ...
CHAPTER 5
Physical AI & Embodied AI
“The ChatGPT moment for Physical AI is nearly here,” said Jensen Huang of NVidia at CES 2026 earlier this year. The statement captures a broader inflection point in how AI is moving off screens and into the physical world. Physical AI—and to a certain extent, embodied AI— ...
CHAPTER 6
Edge MLOps & Orchestration
Modern enterprises increasingly operate at the intersection of data, connectivity, and intelligence, and many use cases require models to run close to where data is generated. As a result, the Edge machine learning operations (MLOps) paradigm has emerged as an architectura ...
CHAPTER 7
Connectivity & Collaborative Learning
Modern connectivity is increasingly moving beyond simple data transport to become a substrate for collaborative learning at the edge. Mainstream networking technologies, such as 5G, Wi-Fi, and Bluetooth, are now combined with edge-native paradigms including Multi-access Ed ...
CHAPTER 8
Hyper-Personalization & Contextual Edge AI
Over the past couple of years, the user experience of edge AI has transcended reactive command-and-control interfaces to become predictive, adaptive, and agentic. This shift is powered by hyper-personalization and contextual AI, systems that do not merely process user inpu ...
CHAPTER 9
Trust Stack: Security, Privacy, Explainability
Processing data at the edge improves latency, cost, and energy use, but it can also increase exposure. Once a sensor or microcontroller begins inferring outcomes, every decision carries operational and legal implications. That’s why edge researchers and designers are worki ...
CHAPTER 10