AI is revolutionizing our lifestyles with constant innovation in deep learning and machine learning driving new use cases across home, retail, and factories. AI at the edge is instrumental for continued success of AI delivering low latency, privacy, and better user experience. The key AI function that happens in an embedded edge device is inference. This is where Texas Instruments (TI) is innovating with TDA4x processor family specially designed to make greener, smarter, and safer edgeAI devices possible.
With industry-leading vision and AI accelerators, TDA4x processors achieve more than 60% higher deep learning performance and energy efficiency compared to leading GPU based architectures. With process and technology leadership, developers can achieve more than six times better deep learning performance compared to leading FPGA based architectures that exist today.
This application note uses the industry standard performance and power benchmarking used to compare the TDA4x system-on-chip (SoC) with other architectures. TDA4x processor family also comes with easy to use, no-cost to low-cost development platforms making it easier for developers to innovate with AI even without any prior experience.
The world population today is 7.8 billion and is on the constant rise with an estimate of 10 billion by 2050 [1]. The growing population needs necessities such as food, clothing and ever-increasing comforts and tools - safely and securely. There is constant technology innovation across all markets - consumer, industrial and automotive to meet these needs. New technologies that we all got used to have made the data generation more affordable and more fun. Think about the number of pictures taken with smart phones and the amount of data being generated from various sensors and edge devices in buildings and factories. All this data is propelling end-to-end automation in factories and buildings to drive productivity to produce more goods and services. This exponentially increases the data that has to be managed - processed, analyzed to take corrective actions. For example, a smart factory could have more than 50,000 sensors and generate several petabytes a day. Even a standard office building will generate hundreds of gigabytes of data. Most of this data will be stored, managed, analyzed and kept right where it was produced, at the edge driven by security, real-time performance and reliability