Machine Learning

Neuromorphic NPU Sips Power to Handle ML on the Edge
Machine Learning

Neuromorphic NPU Sips Power to Handle Edge Machine-Learning Models

BrainChip’s Akida Pico neural processing unit, which leverages spiking neural networks, targets low-power IoT and edge-computing devices.
ThinkStock
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TechXchange

Edge Computing and TinyML

Edge computing often had limited power, and compute resources for machine-learning support needs to fit within these constraints.
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Machine Learning

Bigger Might Not Be Better When It Comes to AI on the Edge

Right-sizing hardware to balance performance, power consumption, and cost enables machine learning to run on resource-constrained platforms.
Dreamstime
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Machine Learning

Generative AI at the Edge: Engineering Challenges

The increasing demands for generative AI at the edge call for new solutions that can achieve high performance within the low-power requirements for edge apps.
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TechXchange

TinyML - What is it?

TinyML is a concept but one that fits a number of machine learning implementations
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Inside Electronics

Advanced Infrastructures for Demanding Data Center and AI SoC Apps

Rambus’ Lou Ternullo weighs in on generative AI fostering a new era of memory innovation and how the company’s PCIe 7.0 IP portfolio tackles emerging challenges in this space....
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TechXchange

AI on the Edge

Artificial intelligence requires compute horsepower, but more efficient algorithms and specialized hardware have made it practical for edge nodes.
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Embedded

Latest Development Boards Push the Boundaries with AI

Gain insight into the development boards leading the way in driving innovation as AI technologies ramp up across the industry.
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Machine Learning

The Best SBCs with Integrated AI Hardware

Discover how SBCs with integrated AI processing power are bringing new capabilities to many industries.