Build Your Expertise on IP for Artificial Intelligence

We've collected the best IP webinars, videos, and white papers in one place to help you enhance your knowledge on designing SoCs for AI applications. Whether your chip design targets next-generation cars, brings AR/VR to life, or enables massive data in the cloud, the DesignWare IP University resources will help you create the SoC your market needs.




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 Growing Market for Specialized Intelligence IP in SoCs
As AI capabilities enter new markets, the IP selected for integration is providing the critical components of an AI SoC. Beyond the IP, designers are finding a clear advantage in leveraging AI expertise, services, and tools to ensure the design is delivered on time, with a high level of quality and value to the end customer for new and innovative applications.

Neuromorphic Computing Drives the Landscape of Emerging Memories for Artificial Intelligence SoCs
The pace of deep machine learning and artificial intelligence (AI) is changing the world of computing at all levels of hardware architecture, software, chip manufacturing, and system packaging. Designers can take advantage of new techniques that rely on intensive computing and massive amounts of distributed memory to offer new, powerful compute capabilities.

Say Welcome to the Machine - Low-Power Machine Learning for Smart IoT ...
This white paper presents a programmable processor and an associated software library for the efficient implementation of low/mid-end machine learning inference.

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.