Scalable UHD H.264 Encoder - Ultra-High Throughput, Full Motion Estimation engine
Big.LITTLE processing with ARM Cortex-A15 & Cortex-A7
Peter Greenhalgh, ARM
EETimes (10/24/2011 4:33 PM EDT)
This white paper presents the rationale and design behind the first big.LITTLE system from ARM based on the high-performance Cortex-A15 processor, the energy efficient Cortex-A7 processor, the coherent CCI-400 interconnect and supporting IP.
The range of performance being demanded from modern, high-performance, mobile platforms is unprecedented. Users require platforms to be accomplished at high processing intensity tasks such as gaming and web browsing while providing long battery life for low processing intensity tasks such as texting, e-mail and audio.
In the first big.LITTLE system from ARM a ‘big’ ARM Cortex-A15 processor is paired with a ‘LITTLE’ Cortex-A7 processor to create a system that can accomplish both high intensity and low intensity tasks in the most energy efficient manner. By coherently connecting the Cortex-A15 and Cortex-A7 processors via the CCI-400 coherent interconnect the system is flexible enough to support a variety of big.LITTLE use models, which can be tailored to the processing requirements of the tasks.
E-mail This Article | Printer-Friendly Page |
|
Arm Ltd Hot IP
Related Articles
New Articles
- Quantum Readiness Considerations for Suppliers and Manufacturers
- A Rad Hard ASIC Design Approach: Triple Modular Redundancy (TMR)
- Early Interactive Short Isolation for Faster SoC Verification
- The Ideal Crypto Coprocessor with Root of Trust to Support Customer Complete Full Chip Evaluation: PUFcc gained SESIP and PSA Certified™ Level 3 RoT Component Certification
- Advanced Packaging and Chiplets Can Be for Everyone
Most Popular
- System Verilog Macro: A Powerful Feature for Design Verification Projects
- System Verilog Assertions Simplified
- Smart Tracking of SoC Verification Progress Using Synopsys' Hierarchical Verification Plan (HVP)
- Dynamic Memory Allocation and Fragmentation in C and C++
- Synthesis Methodology & Netlist Qualification