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Embedded Vision Insights: June 18, 2019 Edition

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RESOURCE-CONSTRAINED DEEP LEARNING IMPLEMENTATIONS

Deep Learning on Arm Cortex-M MicrocontrollersArm
Deep learning algorithms are gaining popularity in IoT edge devices due to their human-level accuracy in many tasks, such as image classification and speech recognition. As a result, there is increasing interest in deploying neural networks (NNs) on the types of low-power processors found in always-on systems, such as those based on Arm Cortex-M microcontrollers. In this talk, Vikas Chandra, Senior Principal Engineer and Director of Machine Learning at Arm, introduces the challenges of deploying neural networks on microcontrollers with limited memory and compute resources and power budgets. He introduces CMSIS-NN, a library of optimized software kernels to enable deployment of neural networks on Cortex-M cores. He also presents techniques for NN algorithm exploration to develop lightweight models suitable for resource constrained systems, using image classification as an example.

Neural Network Compiler: Enabling Rapid Deployment of DNNs on Low-Cost, Low-Power ProcessorsCadence
The use of deep neural networks (DNNs) has accelerated in recent years, with DNNs making their way into...