DesignWare IP University: Artificial Intelligence
tinyML Talks Webcast: Using TensorFlow Lite for Microcontrollers for High-Efficiency
Deeply-embedded AIoT applications doing neural network (NN) inference need to achieve specified real-time performance requirements on systems with limited memory and power budget. Meanwhile, developers want a convenient way of migrating their NN graph designs to an embedded environment. In this talk, we will describe how specific hardware extensions on embedded processors can vastly improve the performance of NN inference operations, which allows targets to be met while consuming less power. We will then show how optimized NN inference libraries can be integrated with well-known ML front-ends to facilitate development flows. To illustrate these concepts, we’ll show the Synopsys MLI Machine Learning Inference library running on a DSP-enhanced DesignWare ARC EM processor.
The Impact of AI on Autonomous Vehicles
Automotive systems designers initially used traditional embedded-vision algorithms in advanced driver assistance systems (ADAS). One of the key enablers of vehicle autonomy moving forward will be the application of artificial intelligence (AI) techniques, particularly those based upon deep-learning algorithms implemented on multi-layer convolutional neural networks (CNNs). These algorithms show great promise in the kind of object recognition, segmentation, and classification tasks necessary for vehicle autonomy.
Efficient Low-Cost Implementation of NB-IoT for Smart Applications
This white paper highlights the key challenges of NB-IoT modem design. It proposes a hardware/software architecture concept based on a single small CPU/DSP processor for executing a NB-IoT software stack. We detail the DSP capabilities of such processors and illustrate their effective use with efficient implementations of key NB-IoT software kernels.