Wevolver spoke with John Weil, Vice President of IoT and Edge AI Processor Business at Synaptics, to understand the challenges of bringing AI to the edge, how the Synaptics Astra™AI Native platform enables cognitive computing in real-world devices, and what the future holds for edge intelligence.
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AI and ML-driven cashierless stores not only improve operational efficiency and customer personalization but are also expected to propel the global AI retail market to over $40 billion by 2030.
AI is transforming security through real-time threat detection and intelligent surveillance, with Axelera AI advancing this shift using its Metis AI Processing Unit (AIPU) to enhance proactive, adaptive, and sustainable security solutions.
The guide to understanding the current state of the art in hardware & software for Edge AI.
In an interview from CES 2025, Vikram Gupta, Chief Product Officer at Synaptics, discusses the company's strategic collaboration with Google to enhance Edge AI processors, emphasizing the importance of context-aware computing and AI-native platforms for improving user experience and efficiency.
In the final chapter of the Edge AI Technology Report: Generative AI Edition explores the technical hurdles organizations face as they attempt to leverage edge-based generative AI. It also examines strategic opportunities for innovation in hardware, deployment configurations, and security measures.
webinar | JAN 15, 2025
Join us for The Rise of Generative AI at the Edge: From Data Centers to Devices webinar on Wednesday, January 15th
Edge AI Technology Report: Generative AI Edition Chapter 3. Advances in model optimization techniques like pruning, quantization, and knowledge distillation pave the way for more efficient deployment of generative AI at the edge.
How BrainChip's Akida™ Edge Box Simplifies ML Deployment
Edge AI Technology Report: Generative AI Edition Chapter 2. Generative AI and edge computing are transforming industries by enabling low-latency, real-time AI on edge devices, allowing efficient, private, and personalized applications without reliance on data centers.
Edge AI Technology Report: Generative AI Edition Chapter 1. Generative AI's demand for real-time insights is driving a shift from cloud to edge computing, enabling faster, local processing on devices and reducing cloud latency and bandwidth constraints.
Unleashing the Power of GPU, understanding the essential strategies for efficient Deep Learning Model Training without compromising on memory and hardware resources in Graphical Processing Units
Remote satellite maintenance is crucial to critical data acquisition and global communication
