How to exploit 17 tried and true DSP power optimization techniques for wireless applications

Code size, speed and power consumption all have a significant impact on the the system-level product that integrates a DSP. The more power an embedded application consumes, for example, the larger the battery or fan required to drive it.
To reduce power, an application must run in as few cycles as possible because each cycle consumes a measurable amount of energy. In this sense, performance and power optimization are similar�using the least number of cycles is an excellent way to meet both performance and power optimization goals.
Although performance and power optimization strategies may share a similar goal, there are subtle differences in how those goals are achieved. This article will explore those differences from the perspective of wireless system design and it will discuss the resulting strategies.
To read the full article, click here
Related Semiconductor IP
- PUF FPGA-Xilinx Premium with key wrap
- ASIL-B Ready PUF Hardware Premium with key wrap and certification support
- ASIL-B Ready PUF Hardware Base
- PUF Software Premium with key wrap and certification support
- PUF Hardware Premium with key wrap and certification support
Related White Papers
- Paving the way for the next generation of audio codec for True Wireless Stereo (TWS) applications - PART 5 : Cutting time to market in a safe and timely manner
- How silicon and circuit optimizations help FPGAs offer lower size, power and cost in video bridging applications
- How Low Can You Go? Pushing the Limits of Transistors - Deep Low Voltage Enablement of Embedded Memories and Logic Libraries to Achieve Extreme Low Power
- Compiler optimization for DSP applications
Latest White Papers
- e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications
- How to design secure SoCs, Part II: Key Management
- Seven Key Advantages of Implementing eFPGA with Soft IP vs. Hard IP
- Hardware vs. Software Implementation of Warp-Level Features in Vortex RISC-V GPU
- Data Movement Is the Energy Bottleneck of Today’s SoCs