Industrial AI & MACHINE LEARNING
 

Friday 2/21

 
This blog discusses how to benchmark inference accelerators to find the one that is the best for your neural network; and how customers commonly evolve their thinking on benchmarking as they come up the learning curve. Neural Network Inference is exciting but complicated, so it is initially very confusing. The lights come on step-by-step as customers work through the issues.
 
 
The latest generation of industrial robots are known as autonomous mobile robots (AMRs). These robots employ a fusion of sophisticated sensor systems, like global positioning system (GPS) and computer vision, augmented with state-of-the-art artificial intelligence (AI) and deep learning (DL) technologies. These technologies allow AMRs to perform object detection and recognition and navigate their way around an uncontrolled environment in which the landscape may be constantly changing.
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SEGGER's stand-alone Floating-Point Library has been extended with an assembly-optimized variant for RISC-V implementations, offering a complete set of high-level mathematical functions in C, using advanced algorithms to maximize performance.
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SiFive?s E31 Core on the Learn Inventor kit is based on RISC-V?s RV32IMAC ISA implementation, which is an acronym special that means it?s a 32-bit core with 32 integer registers that supports multiplication and division, and the atomic and compressed extensions for more portable, dense software.
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IoT Device Security Conference
 
Machine learning (ML) algorithms are moving processing to the IoT device due to challenges with latency, power consumption, cost, network, bandwidth, reliability, security, and more.
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There are solutions that exist in the sweet spot of the Venn diagram between artificial intelligence and embedded engineering. Tune into this week's Embedded Toolbox to learn more. It's a knockout
 
 

 
 
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