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Articles by Nebu Philips
Edge AI Computing: computación distribuida de IA para descargar la nube y optimizar la experiencia del usuario
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Building Advanced Edge-Computing Solutions
The cloud and IoT are growing and expanding in complexity in a variety of ways, as the core technologies behind the devices and related infrastructure evolve and mature. In this podcast, we talk to Nebu Philips, Sr. Director, Strategy & Business Development at Synaptics, about where we are and where we want to go with edge computing, AI, and the cloud. Also discussed is the Synaptics Astra Machina Foundation Series, a modular developer kit for rapid prototyping.
The Rise Of Scalable AI SoCs For The IoT Device Edge
The landscape of computing is undergoing a profound transformation, with Artificial Intelligence (AI) at its forefront. This shift is particularly evident at the device edge, where traditional System-on-Chip (SoCs) implementations are being reimagined to effectively support demanding AI and machine learning (ML) workloads.
Making Edge AI work: why industry collaboration is key
Collaboration is key to growing Edge AI development, writes Nebu Philips, Senior Director, Technical Product Marketing, Synaptics Edge AI application developers aim for a sweet spot for processing performance, but there is a lack of suitable silicon in that performance range. Appropriate development kits are limited, and with the market still maturing, customers can be wary of vendor lock-in. Supporting the adoption of open-source technologies is essential.
Six Challenges of Integrating Edge AI into Industrial Vision Systems
Industrial manufacturers are increasingly turning to machine learning and AI-powered vision systems to improve both safety and quality. In sectors like pharmaceuticals, electronics, and automotive, AI-based inspection detects anomalies, ensures product consistency, and validates compliance with safety protocols. But to meet the strict latency, bandwidth and privacy requirements of these use cases, inference must happen at the edge. This is where development becomes complex.
How to shift the AI load from data centres to the Edge
Nebu Philips, Senior Director, Strategy and Business Development, Synaptics writes about the advantages of moving AI workloads to the Edge The fundamental difference between the core and the Edge is the availability of resources. At the core, compute capabilities, power, memory, and space are abundant, while at the Edge, they are scarce. However, there is omnipresent pressure to shift artificial intelligence (AI) workloads from the core of the network to the Edge.
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