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A quantum trick is shrinking bloated AI models fast
Artificial intelligence has grown so large and power hungry that even cutting edge data centers strain to keep up, yet a technique borrowed from quantum physics is starting to carve these systems down ...
Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
The Internet of Things (IoT) has shown significant growth and promise, with data generated by IoT devices alone expected to reach 73.1 zettabytes by 2025. Moving this data away from its point of ...
“Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. However, constraints from the automotive industry challenge the deployment of CNNs by ...
The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...
Some answers to key edge AI implementation questions, from modeling to hardware selection. The first step in edge AI development: Determine if your application is appropriate for this technology. If ...
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