Understanding Android's strengths and weaknesses
Juan Gonzales, Darren Etheridge, and Niclas Anderberg, Texas Instruments
EETimes (9/29/2011 9:09 AM EDT)
Here are techniques for exploiting Android's strengths and managing its limitations, especially in hard real-time, mission-critical systems.
The details surrounding the meteroic rise of Android in the smartphone market are well documented. However, another revolution is taking place in other applications where Android provides distinct advantages over a "standard" Linux distribution. Android provides a tightly coupled environment for application development where the frameworks and middleware components are selected by Google. Traditional Linux distributions are typically "mix and match" (for example, some people prefer X11/KDE rather than Qt/embedded for graphics development), which burdens the software designer with the need to invest time in understanding the very complex options and make difficult choices that typically have an impact through the product's lifecycle.
For these and other reasons, many refer to Android as "Linux made easy." Today, even Windows Compact Embedded (WinCE) developers who once shied away from Linux due to its complexity are taking a second look at well-integrated Android solutions. Add into the mix a platform licensing approach free from "copy-left" burdens, also known as a method for making a program free, along with a cost you cannot beat (free), and you have Android's recipe for success.
To read the full article, click here
Related Semiconductor IP
- Peripheral Sensor Interface (PSI5) Host Controller
- Link Acceleration Unit
- 64-bit, RISC-V, ultra-high performance processors
- 64-bit, RISC-V, performance and data computation processors
- 32-bit, RISC-V, deeply embedded processors
Related Articles
- Semiconductor industry strengths and weaknesses in the Asia Pacific region
- Optimize drive strengths to reduce power problems
- Android, Linux and Real-Time Development for Embedded Systems
- Android hardware-software design using virtual prototypes - Part 2: Building a sensor subsystem
Latest Articles
- Design and Development of a Neuromorphic Silicon Suite: PVT Sensing, Stochastic LIF Inference, On-Chip STDP Learning, and Crossbar Programming
- LLM4RTL: Tool-Assisted LLM for RTL Generation
- Towards Delta Aware Training: Efficient DNN Weight Storage for Resource-Constrained FPGAs
- CHERI-D: Secure and efficient inline object ID for CHERI temporal memory safety
- AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing